{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_tasks(app_name, droidagent_result_dir):\n",
    "    task_row = []\n",
    "    with open(os.path.join(droidagent_result_dir, 'exp_data.json')) as f:\n",
    "        exp_data = json.load(f)\n",
    "\n",
    "    task_results = exp_data['task_results']\n",
    "    for task, task_data in task_results.items():\n",
    "        num_actions = 0\n",
    "        for entry in task_data['task_execution_history']:\n",
    "            if entry['type'] == 'ACTION' and entry['action_data'] is not None:\n",
    "                num_actions += 1\n",
    "\n",
    "        task_row.append({\n",
    "            'app_name': app_name,\n",
    "            'task': task,\n",
    "            'success': task_data['result'] == 'SUCCESS',\n",
    "            'num_actions': num_actions,\n",
    "            'num_critiques': task_data['num_critiques'],\n",
    "            'num_visited_pages': len(task_data['visited_pages_during_task']),\n",
    "        })\n",
    "\n",
    "    return task_row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_rows = []\n",
    "for app_name in os.listdir('../data/'):\n",
    "    if app_name == \"QuickChat\":\n",
    "        continue\n",
    "    if app_name == '.keep':\n",
    "        continue\n",
    "\n",
    "    result_path = os.path.join('../data/', app_name)\n",
    "\n",
    "    task_row = analyze_tasks(app_name, result_path)\n",
    "    task_rows.extend(task_row)\n",
    "\n",
    "task_df = pd.DataFrame(task_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_name</th>\n",
       "      <th>task</th>\n",
       "      <th>success</th>\n",
       "      <th>num_actions</th>\n",
       "      <th>num_critiques</th>\n",
       "      <th>num_visited_pages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>Complete the AppIntro by clicking on the \"GET ...</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>Start playing a song from the song list.</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>Explore the album details by selecting an albu...</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>Edit the tags of the current album on the Albu...</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>Set a sleep timer for the current playing album.</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>Set a password for the notes to secure them.</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>Navigate to the Gallery page to view attached ...</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>Add a new tag \"Study Material\" to the note tit...</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>Add a new tag \"CS Studies\" to the note titled ...</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>Share the note titled \"Meeting Notes\" with a c...</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>547 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       app_name                                               task  success  \\\n",
       "0    Phonograph  Complete the AppIntro by clicking on the \"GET ...     True   \n",
       "1    Phonograph           Start playing a song from the song list.     True   \n",
       "2    Phonograph  Explore the album details by selecting an albu...     True   \n",
       "3    Phonograph  Edit the tags of the current album on the Albu...     True   \n",
       "4    Phonograph   Set a sleep timer for the current playing album.     True   \n",
       "..          ...                                                ...      ...   \n",
       "542  Omni-Notes       Set a password for the notes to secure them.    False   \n",
       "543  Omni-Notes  Navigate to the Gallery page to view attached ...    False   \n",
       "544  Omni-Notes  Add a new tag \"Study Material\" to the note tit...    False   \n",
       "545  Omni-Notes  Add a new tag \"CS Studies\" to the note titled ...    False   \n",
       "546  Omni-Notes  Share the note titled \"Meeting Notes\" with a c...    False   \n",
       "\n",
       "     num_actions  num_critiques  num_visited_pages  \n",
       "0              1              0                  2  \n",
       "1              1              0                  1  \n",
       "2              4              1                  2  \n",
       "3             13              3                  3  \n",
       "4              4              1                  1  \n",
       "..           ...            ...                ...  \n",
       "542           13              3                  1  \n",
       "543           13              3                  1  \n",
       "544           13              3                  1  \n",
       "545           13              3                  1  \n",
       "546           12              3                  1  \n",
       "\n",
       "[547 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_name</th>\n",
       "      <th>num_success</th>\n",
       "      <th>num_failed</th>\n",
       "      <th>task_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APhotoManager</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AntennaPod</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Markor</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MaterialFB</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MyExpenses</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OpenTracks</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scarlet-Notes</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>collect</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commons</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>openlauncher</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>osmeditor4android</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             app_name  num_success  num_failed  task_count\n",
       "0       APhotoManager           15          26          41\n",
       "1       ActivityDiary           25          13          38\n",
       "2           AnkiDroid           26          18          44\n",
       "3          AntennaPod           29          16          45\n",
       "4              Markor           12          16          28\n",
       "5          MaterialFB            9          15          24\n",
       "6          MyExpenses           21          21          42\n",
       "7          Omni-Notes           10          24          34\n",
       "8          OpenTracks           31           7          38\n",
       "9          Phonograph           16          17          33\n",
       "10      Scarlet-Notes            7          13          20\n",
       "11            collect           27          26          53\n",
       "12            commons           14          18          32\n",
       "13       openlauncher           10          21          31\n",
       "14  osmeditor4android           25          19          44"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Draw stacked bar chart about number of succeeded tasks and number of failed tasks\n",
    "\n",
    "task_df['task_count'] = 1\n",
    "task_df_agg = task_df.groupby(['app_name']).sum()\n",
    "task_df_agg = task_df_agg.reset_index()\n",
    "task_df_agg['num_success'] = task_df_agg['success']\n",
    "task_df_agg['num_failed'] = task_df_agg['task_count'] - task_df_agg['success']\n",
    "task_df_agg = task_df_agg[['app_name', 'num_success', 'num_failed', 'task_count']]\n",
    "task_df_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_success</th>\n",
       "      <th>num_failed</th>\n",
       "      <th>task_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.466667</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>36.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.166715</td>\n",
       "      <td>5.154748</td>\n",
       "      <td>8.790146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_success  num_failed  task_count\n",
       "count    15.000000   15.000000   15.000000\n",
       "mean     18.466667   18.000000   36.466667\n",
       "std       8.166715    5.154748    8.790146\n",
       "min       7.000000    7.000000   20.000000\n",
       "25%      11.000000   15.500000   31.500000\n",
       "50%      16.000000   18.000000   38.000000\n",
       "75%      25.500000   21.000000   43.000000\n",
       "max      31.000000   26.000000   53.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df_agg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_name</th>\n",
       "      <th>num_success</th>\n",
       "      <th>num_failed</th>\n",
       "      <th>task_count</th>\n",
       "      <th>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APhotoManager</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AntennaPod</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Markor</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MaterialFB</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MyExpenses</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>42</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OpenTracks</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scarlet-Notes</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>collect</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commons</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>openlauncher</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>osmeditor4android</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total</td>\n",
       "      <td>277</td>\n",
       "      <td>270</td>\n",
       "      <td>547</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             app_name  num_success  num_failed  task_count  success_rate\n",
       "0       APhotoManager           15          26          41          0.37\n",
       "1       ActivityDiary           25          13          38          0.66\n",
       "2           AnkiDroid           26          18          44          0.59\n",
       "3          AntennaPod           29          16          45          0.64\n",
       "4              Markor           12          16          28          0.43\n",
       "5          MaterialFB            9          15          24          0.38\n",
       "6          MyExpenses           21          21          42          0.50\n",
       "7          Omni-Notes           10          24          34          0.29\n",
       "8          OpenTracks           31           7          38          0.82\n",
       "9          Phonograph           16          17          33          0.48\n",
       "10      Scarlet-Notes            7          13          20          0.35\n",
       "11            collect           27          26          53          0.51\n",
       "12            commons           14          18          32          0.44\n",
       "13       openlauncher           10          21          31          0.32\n",
       "14  osmeditor4android           25          19          44          0.57\n",
       "0               total          277         270         547          0.51"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add total row (app name: total)\n",
    "total_row = task_df_agg[['num_success', 'num_failed', 'task_count']].sum()\n",
    "total_row['app_name'] = 'total'\n",
    "\n",
    "task_df_agg = pd.concat([task_df_agg, pd.DataFrame([total_row])])\n",
    "task_df_agg['success_rate'] = task_df_agg['num_success'] / task_df_agg['task_count']\n",
    "task_df_agg.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_name</th>\n",
       "      <th>num_success</th>\n",
       "      <th>num_failed</th>\n",
       "      <th>task_count</th>\n",
       "      <th>success_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>total</td>\n",
       "      <td>277</td>\n",
       "      <td>270</td>\n",
       "      <td>547</td>\n",
       "      <td>0.506399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>collect</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>53</td>\n",
       "      <td>0.509434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AntennaPod</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>0.644444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "      <td>0.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>osmeditor4android</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "      <td>0.568182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MyExpenses</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>42</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APhotoManager</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "      <td>0.365854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>38</td>\n",
       "      <td>0.657895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OpenTracks</td>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>0.815789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Omni-Notes</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>0.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commons</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>32</td>\n",
       "      <td>0.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>openlauncher</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>0.322581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Markor</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MaterialFB</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scarlet-Notes</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             app_name  num_success  num_failed  task_count  success_rate\n",
       "0               total          277         270         547      0.506399\n",
       "11            collect           27          26          53      0.509434\n",
       "3          AntennaPod           29          16          45      0.644444\n",
       "2           AnkiDroid           26          18          44      0.590909\n",
       "14  osmeditor4android           25          19          44      0.568182\n",
       "6          MyExpenses           21          21          42      0.500000\n",
       "0       APhotoManager           15          26          41      0.365854\n",
       "1       ActivityDiary           25          13          38      0.657895\n",
       "8          OpenTracks           31           7          38      0.815789\n",
       "7          Omni-Notes           10          24          34      0.294118\n",
       "9          Phonograph           16          17          33      0.484848\n",
       "12            commons           14          18          32      0.437500\n",
       "13       openlauncher           10          21          31      0.322581\n",
       "4              Markor           12          16          28      0.428571\n",
       "5          MaterialFB            9          15          24      0.375000\n",
       "10      Scarlet-Notes            7          13          20      0.350000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df_agg = task_df_agg.sort_values(by='task_count', ascending=False)\n",
    "task_df_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oXbs2qlWrhkqVKkFdXR1HjhxJNykGgLZt26Jt27aIjo4WK0J7e3sjODgYJ0+exLdv38Q10j9asmQJevXqhbi4OFy+fBmzZs2Cm5ubzKh7TvsgIlJmXFNMRETiNGoAOHfuHNzd3dNtl1oQK6P9Sj9+/Jg3AWYidQrszwRBwJMnTwBALMKVWjQpJCQk3SJaqdfdvHlTZkpuTlSsWFGcAp7ZKGbquUqVKuW4z1Sp08XTk7pmtGrVqpk+xvbt25GSkoIGDRrgwIEDGDhwIOzs7MSEODExMd+KtGXExMREfG/+WETrZw8ePMCzZ8/EJDj1/RAbG5vhtPXMXsPsunfvnri+fOvWrZg5cyY6duyIqlWriiPjHz58SHNdfHy8OHUd+D6roFWrVpg5cybOnTsnfna9vLxkRs1TdevWDQCwYMEC6OjoIDg4GP/880+u9kFEpMyYFBMREQCIyQ4Acc/Wn5UqVQrA91HN9NYmXrx4Me8CzMDVq1fTraB86dIlfPjwARoaGmIFY1tbW5QoUQIxMTEZjhSfOnUKQ4YMQceOHdNNULJLW1sbDRo0APB9H9j0hIaGwtPTE0D6xcbk5e3tjU+fPqU57uXlhffv30NDQwOtWrXK9DHevHkDALC0tEx3f1o3NzdxGnBGNxryWvHixcX3bmavcf/+/eHg4IDz588D+F5cqmbNmgC+F8D6WUpKCo4dO5bteH5cBvDjrIrU1xJIf315XFycWH38x3XWhw8fRteuXTF9+vR0Z2k0atRI/O/01menKl++vJjc7t69G/fu3cv1PoiIlBGTYiIiAvB/06i1tbUznB5du3ZtqKurQxAEODo6imt1k5KSsGfPHhw5ciQ/QwbwPZH4448/xG2XgO/bz8yZMwfA94rPqaOa2tra4hZPS5cuxbFjx2QKGl26dAnz588HAHTs2BEVK1bMlRjHjx8PNTU1XL16FX///bfMOuOnT59i5MiRSEhIgKWlpTiqlxvi4uIwduxYvH//Xjx269YtzJo1CwAwatSoX64PrVKlCgDgzJkzYsEy4Pu04n379mHJkiXisR8rJue3CRMmQFVVFadPn8ayZctkpkQ/f/4co0aNQlJSEipUqCCz9dLUqVMBAHv37oWzs7P4foiLi8Pff/+Nhw8fZjuWH6dzpxb3Av7vtQSATZs2yawpDgoKwsiRI8XCZz8uRejYsSPU1dXx/PlzODo6ykyLj4iIwLp16wB8/3ymTgnPSP/+/WFjY4OUlBTMmjVLnB6em30QESkbrikmIiKRiYkJ/vzzzwy3ZypZsiSGDx+Of//9F6dPn4aPjw+MjY3x9u1bfP36Ff369YOnp+cv977NTS1btsS1a9fQrl07VK1aFXFxcWJBqLZt22Ly5Mky7UeOHInQ0FAcOXIEs2fPxqpVq2BsbIywsDBx+reNjQ2WLl2aazHWrVsXS5cuxdy5c3HkyBGcPHkSZmZmMtN2zc3N4eTklGHlZHlUrlwZT548QZs2bWBubo7Y2Fgx6frtt98wevToXz7GuHHjcP36dXz69AldunRB5cqVoaGhgVevXiE2Nhb6+vowNTXF06dPc2VkXV42NjZYvHgx5s+fD2dnZxw6dAhmZmaIiYnBq1evIAgCDAwMsHPnTpnXuEmTJpg2bRrWrFmDZcuWYfv27ShfvjyCg4MRExODtm3bZnsGhJ6eHipUqIC3b99i3LhxqFKlCiZNmoRmzZqJBa927dqF//77D8bGxvj69as4ity4cWNcu3YNMTExiI6ORvHixWFoaAhHR0dMnz4dLi4uOHr0KCpWrAipVIrXr18jISEBpUqVytJ7ViKRYPHixejWrRuCg4Oxfv16/PXXX7naBxGRsuFIMRERyRgwYIA4FTU9U6ZMwerVq2FjY4OkpCS8fPkSpqamWLVqFRYsWJB/gf5/9erVw6FDh9CoUSO8efMGHz9+RO3ateHo6IgNGzakqWKcmhTs3LkTbdu2hZqaGp48eYKYmBjUqVMHc+fOhbOz8y8rZWdXt27dcOLECfTu3RsGBgYIDAzEly9fUK9ePcybNw9Hjx6FiYlJrvZpbW2NgwcPolGjRnj16hW+fPkCOzs7rFu3DmvWrMlSAm5lZYUTJ07AwcEBRkZGeP36NV6/fo2KFStizJgxOH36tLj/8uXLlzOcZZAfevbsiRMnTqBPnz4oU6YMAgMD8f79e5iZmWHEiBE4efKkuI74RyNHjoSLi4u4n3JgYCBMTU2xdu1aDB06VK5Y/vnnH9StWxcpKSkICQnB69evAQBr1qzB4sWLYW1tDUEQ8OzZMyQmJqJly5bYunUrdu3aBSMjIwAQp9QD3/df3rt3L9q3bw9dXV28ePECb9++RaVKlTB69GicPXsW1apVy1JsZmZmGDt2LADA2dkZ/v7+ud4HEZEykQiK/OtFREREuW7mzJk4fvw4unTpgtWrVys6HCIiogKNI8VERERERERUZDEpJiIiIiIioiKLSTEREREREREVWUyKiYiIiIiIqMhioS0iIiIiIiIqsrhPcTb5+/tDEIQ0W3wQERERERFRwZGUlASJRIK6detm2o5JcTYJgqDQPRiJiIiIiIjo17KatzEpzqbUEWJra2sFR0JEREREREQZefjwYZbasdAWERERERERFVlMiomIiIiIiKjIYlJMRERERERERVaeJcXR0dGIiorKq4cnIiIiIiIiyrEcJcWCIODs2bO4dOmSeCw6Ohpjx46Fra0t7O3t0bdvX7x48SLHgRIRERERERHlNrmT4qSkJAwZMgR//vknTp8+LR5ftGgRPD09xa2L7t27h8GDB+Pr16+5ES8RERERERFRrpE7KXZ1dcXt27ehqakJCwsLAMCXL19w9uxZSCQSTJ06FceOHUPjxo0REREBZ2fn3IqZiIiIiIiIKFfInRSfO3cOEokEmzdvxh9//AEA8Pb2RnJyMqpUqYJRo0ahZs2aWLVqFTQ0NODl5ZVrQSszQSotEn0SEREREREpAzV5LwwMDESFChXQqFEj8di1a9cgkUjQrFkz8Zi+vj4qVqyIN2/e5CzSQkKiqoqI8ROQHBiUL/2pVasKfaeN+dIXERERERGRspE7KY6OjoaJiYnMsRs3bgAA7O3tZY6rqqoiMTFR3q4KneTAICQ9eqToMIiIiIiIMpWUlAQpZx2SgqmoqEBdXR0SiSRPHl/upFhXVxcfP34Uf3769CnCw8OhpqYGW1tb8XhcXBxev34NAwODnEVKRERERET5IioqCuHh4UhISFB0KEQAvg+0amtrw9DQEBoaGrn62HInxbVq1YK3tzfOnTuHjh07ioW0bG1toaOjI7Zbt24dYmNjZaZUExERERFRwRQVFYW3b9+iePHiMDAwyNMROqJfEQQBUqkUcXFxiIyMREhICIyNjaGtrZ1rfcidFA8YMACXL1/G1KlTsWDBAkRFRUEikWDAgAEAvo8cz5gxA4GBgZBIJBg4cGCuBU1ERERERHkjPDwcxYsXh7GxMZNhKjCKFy8OfX19vHr1CuHh4ahYsWKuPbbc1aebNm2K2bNnQ0NDA5GRkVBVVcXo0aPRpk0bAIBEIsHz58+hpqaGlStXon79+rkWNBERERER5b6kpCQkJCSgZMmSTIipwFFVVYW+vj5iYmKQnJyca48r90gxAAwePBjdu3fHy5cvYWxsDH19ffFc5cqVMXv2bHTs2BFlypTJcaBERERERJS3UotqqaurKzgSovRpamoCAJKTk6GmlqN0ViT3SHGqEiVKoFatWjIJMfA92MGDB4sJ8f3793PaFRERERER5QOOElNBlRfvTbmT4rVr12apXWJiIlauXIn+/fvL2xURERERERFRnpB7vHnbtm3Q0NDA+PHjM2zz4MEDzJo1C8HBwfJ2Q0RERERERJRn5E6K1dTUsGnTJmhoaGDUqFEy5xITE7Fhwwbs3r0bUqkUGhoaGDduXI6DJSIiIiIiIspNck+fXr9+PdTU1LBu3Trs3r1bPP7gwQN0794dO3fuhFQqhY2NDdzc3DB69OhcCZiIiIiIiBQrRUhRdAhyUda4KW/JPVLcpk0bbNy4ERMnTsTKlSsBAJ8/fxZHh3V0dPDnn39yLTERERERUSGjIlHBmjurEPotVNGhZJlJCRP8WX96rj9udHQ0Dhw4AE9PT7x8+RIxMTHQ1dWFmZkZWrRogb59+0JHRyfX+6Xck6Ma1i1atICTkxMmTJggJsaCIKBFixZYsGABypUrlytBEhERERFRwRL6LRTBkS8UHYZCBQYGYtiwYfj06RMMDQ1Rp04dFCtWDJ8+fcKjR49w+/Zt7N69G7t27YK5ubmiw6UM5Hhjp2bNmmHLli34448/kJiYiE6dOmW5MjUREREREZEykkqlGD9+PD59+oQpU6Zg9OjRMtsFRUZGYtGiRTh9+jTGjBkDd3d37v9cQGVpTXFKSkqm/2vQoAG2bNkCTU1NnD9/HsePH0+3HRERERERUWHg5+eHkJAQWFpaYsyYMWn2zy1ZsiSWLVuGcuXK4e3bt/D29lZQpPQrWRoprlmzZrYedPbs2Zg9e7bMMYlEgoCAgGw9DhERERERUUEUHh4OAGmS4R9paGhg+PDhePLkCUqUKCEev3XrFgYPHgxLS0ucOHEizXVjx46Fh4cHli1bhh49esicu3LlCg4cOIBHjx4hOjoaFSpUQOvWrTF8+HCULFlSpu27d+/g7OyMy5cv48OHD9DX10fNmjUxevRo1KpVS6atIAg4ceIEjh49iidPniApKQkVK1ZEx44dMWzYMGhra8u0j46Oxvbt23H58mWEhoZCEARUqlQJbdq0wdChQ1G8eHGZ9ufOncPhw4cRFBSEr1+/wsDAALa2tvjf//6H6tWrZ/JK570sJcWCIOR1HEREREREREqjevXqkEgkePLkCRwdHTF69GiULl06TbvBgwfnWp+rVq3Cjh07oKKigrp166J06dK4f/8+tm7diosXL+LgwYPQ09MDANy9exdjx47F169fYWxsjBYtWuDDhw+4dOkSvLy84OTkhFatWgH4PhV8ypQpcHd3h5aWFqytrVGyZEn4+flhw4YNuHDhApydnVGqVCkAQEJCAgYOHIgnT57A0NAQ9vb2EAQBfn5+2LhxIzw8PHDkyBFxuvi2bduwZs0aqKurw8bGBiVLlsSLFy9w8uRJnD9/Hs7OzrCxscm11ym7spQUu7i45HUcRERERERESqNy5cro378/9u/fjz179mDv3r2oVasW6tevDxsbGzH5yy1eXl7YsWMH9PT0sH37dnGkNzExEZMmTYKnpyfWr1+PBQsWIDY2FtOnT8fXr18xfvx4jBs3Dioq31fOnjp1CtOnT8dff/2Fa9euQUNDA1u3boW7uztq1qwJJycnGBkZAQDi4+MxZ84cnD59GvPmzcPGjRsBAO7u7njy5Ans7Oywa9cuMfmNjIxE//79ERAQgIsXL6JTp05ITEzEli1boKamBjc3N1StWlV8TmvXrsXWrVuxZcsW7NixI9deq+zKUlJsZ2eX13EQEREREREplblz56JixYrYtGkToqKicO/ePdy7d08cza1Tpw4GDhyIzp0757ivffv2AQCmTp0qM/VZQ0MDf//9N54/f46vX78C+J5Av337FnXr1sWECRNkHqdLly64cOECPnz4gBcvXsDMzAzOzs4AgDVr1ogJMQAUK1YMixcvxvXr13Hx4kWEhISgcuXKCAsLAwCUK1dOpnhYyZIlsXDhQoSEhKBGjRoAgG/fviE2Nhba2towNDSUiWXUqFEoXbo0qlSpkuPXJydyXH06I9HR0UhJSYGurm5edUFERERERKQwKioqGDp0KPr164erV6/i+vXruHPnDp4/f46UlBT4+fnBz88Pp0+fxj///AMNDQ25+hEEAbdv3wYAtG3bNs15IyMjeHh4iD/funULANCmTZt0Hy91xBcA7t27h8jISBgZGcHU1DRNW21tbdjZ2eH8+fO4desWKleuDHt7ewDAyZMn8eXLF7Rt2xZNmjRBhQoVUL9+fdSvX1+8vnTp0qhWrRoCAwPRvXt3ODg4oFmzZqhVqxaKFy+OIUOGyPGK5K4cJcWCIODcuXPQ0NAQX/Do6GjMmDEDXl5eAIDatWtj6dKlMDMzy3m0REREREREBYympiZat26N1q1bAwCioqJw69YtHDt2DF5eXvD09MTmzZsxefJkuR7/69evSExMhKamJvT19X/Z/uPHjwAgM+qbkXfv3on/b2FhkaW2tWrVwrx587By5Ur4+PjAx8cHwPcp5W3atEHfvn1hYmIiXrd+/XpMmDABwcHB2Lx5MzZv3owSJUqgadOm6Nq1K1q0aPHLOPOS3ElxUlIShg8fDl9fX7Rv315MihctWgRPT0+x3b179zB48GCcOXNGXPRNRERERESkzJ4+fYovX77AxsYmzQiwrq4u2rZti7Zt2+Kff/7B5s2bceLEiSwnxT9vZ5ucnAwg80rXP0pKSspSO+D/iiqXLVv2l8tmf5zmPGDAAHTu3BkeHh7w8fGBr68vQkJCsGPHDuzZswdOTk5islu1alWcOXMGt27dgpeXF27evInnz5/j7NmzOHv2LLp06YLVq1dnOebcJndS7Orqitu3b6NYsWLiHYUvX77g7NmzkEgkmDJlCho3boy1a9fi+vXrcHZ2lvvOCBERERERUUEyfPhwhIeH4+DBg6hXr16G7fr06YPNmzeL630BiEWvpFJputdERkbK/Kynpwd1dXXEx8fjy5cvYhXoH7m5uUFbWxtNmjQR1+5++PAh3cd/8OABgoODUbt2bZQpUwbA91Hl7Camenp66NmzJ3r27Ang+42CDRs2wMPDAytWrJAZAVZRUUHDhg3RsGFDAEBERAROnjyJ1atX49SpUxg0aBBq166drf5zi4q8F547dw4SiQSbN2/GH3/8AQDw9vZGcnIyqlSpglGjRqFmzZpYtWoVNDQ0xOnUREREREREyi51C6E9e/Zk2i44OBgAZKYmp+75Gx4enmb727i4ODx9+lTmmLq6ulhcK7286suXL5g9ezamTp0KAGKSnlEOtnXrVvz111/w9fWFtbU1tLS08OjRI7GA1o8EQcCgQYPQu3dvcV3zxo0b0bx5c5w6dUqmraWlJWbPng0AeP/+PYDvCfhvv/2G0aNHy7TV19fH0KFDUbduXQD/NzVbEeROigMDA1GhQgU0atRIPHbt2jVIJBI0a9ZMPKavr4+KFSvizZs3OYuUiIiIiIiogBg7diw0NTVx/vx5/PXXX/j06VOaNv7+/pgzZw4AYMSIEeJxU1NTaGho4MuXL3BzcxOPJyYmYt68eYiNjU3zWKn7Ha9ZswbPnz8XjyckJGD+/PmQSqX47bffoK2tjc6dO8PAwAC3b9/Gzp07ZR7n7Nmz8PDwgK6uLjp06AAtLS3069cPSUlJmDBhAkJDQ8W2UqkUq1atwu3bt/Hq1StYWVkBAIyNjfHhwwc4OTmled4nTpwAAFhbWwP4PnX67du3uHLlCtzd3WXaBgYG4vHjx1BRUREfWxHknj4dHR0ts3gaAG7cuAEAYjWyVKqqqkhMTJS3K1JCglQKiapqkemXiIiIqKgxKWHy60YFSG7Ha2lpiY0bN2L69Olwc3PDyZMnUbNmTRgZGSE5ORkvXrxASEgI1NTUMHPmTJlK0Nra2hg0aBB27tyJmTNnwtXVFfr6+vD390dsbCzat2+fJoHs0KEDBg0ahL1796J79+6oX78+ihcvjgcPHuDjx48wMzPDjBkzxMdft24dxowZg5UrV+Lo0aOoVq0a3r59i0ePHkFdXR3Lli0TdwqaMmUKnj17hmvXrqFz586wsrKCvr4+Hj9+jHfv3qFYsWLYsGGDOMLt4OCAM2fOwMfHB23btkW9evVQvHhxBAUF4cWLFyhevLg4YqytrY2FCxdi+vTpmDhxImrUqAETExN8/foVd+/eRXJyMsaOHZsmt8xPcifFurq6YlUz4Pv88fDwcKipqcHW1lY8HhcXh9evX8PAwCBnkZJSkaiqImL8BCQHBuVbn2rVqkLfaeOvGxIRERFRjqQIKfiz/nRFh5FtKUIKVCRyT5ZNo3nz5rhw4QIOHz6Mq1evIiQkBM+ePYOamhrKlSuHgQMHol+/fqhatWqaa6dPn44KFSrgyJEjePjwIbS1tdGgQQNMnjwZFy9eTJMUA9/3Rbazs8P+/fvx+PFjxMfHw8jICKNHj8aoUaNQvHhxsa2dnR3c3NywdetWXLt2DZ6entDR0UG7du0wZswY1KxZU2yroaGB7du349ixY3Bzc8OzZ8+QlJSE8uXLo3fv3hg+fDgqV64stldVVYWTkxOcnZ1x/vx5+Pn5ITk5GWXLlkXfvn0xatQoVKhQQWzv4OAAPT097N27Fw8fPsSzZ89QokQJNGzYEP3790erVq1y6V9EPhLh50nsWTRmzBh4e3tj7dq16NixI2bOnAk3Nzc0bNgQu3fvFts5OjrCxcUFHTp0wPr163MrboV5+PAhgP+bDiCPj+07IunRo9wKKVPqVlYwdD+XL339LD+fJ6DY50pERERUGMTHx+Ply5cwNTVFsWLFFB0OURrZeY9mNXeT+zbJgAEDIAgCpk6dCnt7e5w4cQISiQQDBgwA8H3k2MHBAXv37oVEIsHAgQPl7YqIiIiIiIgoT8idFDdt2hSzZ8+GhoYGIiMjoaqqitGjR4tz5SUSCZ4/fw41NTWsXLkS9evXz7WgiQoSIYNS+oWtT0X1q6jnSkRERERFg9xrioHvFdC6d++Oly9fwtjYGPr6+uK5ypUrY/bs2ejYsaO49xVRYZTf66cVuXa6KD1XIiIiIioacpQUA0CJEiXEPbN+pKmpKZYNJyrskgOD8nX9tCIVpedKRERERIVf7pVey0RMTAxOnz6dH10RERERERERZVmORoqfPn2Kf//9F8+fP0d8fDxSUlJkzicnJyM+Ph4xMTGQSCT47bffchQsERERERERUW6SOykOCQlBv379EB8fj6zs6lS+fHl5uyIiIiIiIiLKE3JPn969ezfi4uJgYGCAiRMnYsaMGQCAZs2aYfHixRg3bhwqVaoEAGjcuDE8PT1zJ+L/b+LEibCwsMB///2X7vnr169j2LBhaNiwIerWrYuePXvC1dU1Swk8ERVtRam6NxEREVFRJ/dI8c2bNyGRSLBlyxZYWVkBAHbt2oWoqCj8/vvvAICRI0dixIgRuH79Oq5cuYJmzZrlStCurq5wd3fP8Pz+/fuxaNEiqKurw97eHurq6rh58ybmzp2LO3fuYMWKFbkSBxEVTvldZRtgpW0iIiIiRZE7Kf748SPKly8vJsQAUL16ddy6dQtSqRSqqqooVqwYFixYgN9++w2HDx/OlaT45cuXcHR0zPB8cHAwlixZAl1dXezduxeWlpYAgHfv3mHIkCFwc3ND8+bN0alTpxzHQkSFF6tsExERERUNck+flkqlKF26tMyxypUrIykpCSEhIeKxqlWrwtjYGI9y4ctlYmIi/vzzT6ioqKBGjRrpttm+fTtSUlIwfPhwMSEGACMjI8ybNw/A9xFtIiIiIiIiIrmTYj09PXz58kXmmImJCQAgKCgoTduIiAh5uxKtW7cOjx8/xrx58zIs3HX58mUAQLt27dKca9SoEXR1dfHw4UOEh4fnOB4iImWniHXMXDtNREREBYnc06dr1KgBb29vPH78GDVr1gTwfaRYEATcv38f7du3B/B9W6Y3b95AW1s7R4Fev34du3fvRufOndG1a9d01xSHh4cjIiICmpqaMDU1TXNeVVUVVapUwb179/Ds2TMYGBjkKCYiImWX3+unuXaaiIiIChq5k+IOHTrg8uXLGDlyJMaPH4++ffuiTp06UFNTw8GDB9GyZUvUqFEDTk5O+Pr1K2rVqiV3kBEREZgxYwbKlSuHBQsWZNguLCwMAFCmTBlIJJJ025QpUwYA8OnTJ7njISIqTLh+moiIskuQSiFRVVV0GNmmrHFT3pI7Ke7SpQv+++8/3L59G0uXLkWfPn2gq6uLLl264Pjx4xg8eLDYViKRiBWp5TF79mx8/vwZe/bsga6ubobt4uLiAABaWloZttHU1AQAxMTEyB0PEREREVFRpoidGnIqL2YrvXnzBq1bt85S2xIlSuDOnTty9TNo0CDcvn0bmzZtQps2bQAAM2fOxPHjxzFr1iwMHTpUrsf9lVu3bmHw4MGwtLTEiRMn8qSPgkDupFhVVRXbt2/Htm3bcOPGDaj+/zsuc+fOxdu3b3H79m2xbceOHdGrVy+5+tm/fz+8vLwwcuRI2NnZZdpWRSXrS6S5XzERERERkfw400hWly5dMj2f0+WklHfkToqB76OuEyZMwIQJE8RjOjo6cHFxwf379/HmzRtUqVIF1atXl+vxAwMDsWLFCtSsWROTJk36ZXsdHR0AQHx8fIZtEhISAPBNSUREREREuWf16tV59tgrVqxAXFwcypUrl2d9FGVyJ8W+vr4oUaKEzLZHP6pduzZq164NALh69SpevXqFAQMGZKuP1atXIyEhAcWKFcOsWbNkzj1+/BgAcOTIEVy/fh22trZica/MKkt//PgRAGBoaJitWIiIiIiIiBTByMhI0SEUanInxYMGDUL9+vWxb9++X7Zdv369XElxbGwsAODu3bu4e/duum38/f3h7+8PNTU19OnTB2XLlkVYWBhCQ0PFLaJSSaVSBAcHAwDMzc2zFQsREREREVFuSExMxNGjR3H+/Hk8f/4c3759g5aWFszMzNClSxf0799fZmloemuKM3P37l3s3r0bfn5+iIqKQpkyZdC0aVOMGTMm3QT7y5cv2LFjBy5cuICwsDCUL18effr0kXvGr7LJUlIcHR2dZk9i4Ps05dDQ0AyvEwQBb9++RXBwsFxrePfu3ZvhubFjx8LDwwPLli1Djx49xOMtWrTA4cOHceHCBQwfPlzmmmvXruHbt2+oWbMmR4qJiIiIiCjfJSYmYtiwYbhz5w50dXVRp04dFCtWDK9evcK9e/dw7949BAYGYuHChXI9vrOzM5YvXw4AqFmzJurXr4/AwEAcPnwY7u7u2L59u8zOQGFhYRg8eDBCQkJQtmxZtGjRAmFhYVixYgWqVauWK8+5oMtSUhwTE4MuXbqI63GB7xWlHz9+jHbt2mWpozp16sgVYHb1798fx44dw5YtW2Brayv+g7979w6LFy8GAIwZMyZfYiEiIiIiIvrR4cOHcefOHVhZWcHFxUWsiwQAp06dwrRp03D06FFMnz4dxYsXz9Zj+/r6Yvny5dDV1cXmzZtRv3598dyePXvg6OiICRMmwN3dHcWKFQMAODo6IiQkBJ07d8by5cuhoaEBALh06VKW6joVBlkq11y2bFkMGzYMgiCI/wMg83Nm/ytfvjzmzp2bp08klaWlJaZMmYJv376hX79+GDZsGMaMGYPOnTvj9evX6Nu3b5YTeSIiKhwEqbTI9FtU+iQiKmgsLCwy/N+gQYPEdmpqamjZsiWmT58ukxAD3ytY6+rqIjk5GWFhYdmOYfv27RAEAdOmTZNJiAFgyJAhaNasGT58+IBTp04BAD59+gR3d3cUL14cixcvFhNiAGjTpg369euX7RiUUZbXFI8dO1bcVkkQBLRp0wbW1tZYv359hteoqKhAW1sbJUuWzHGg2TFixAiYmprC2dkZ9+/fh0QigZmZGQYMGICuXbvmayxERKR4ithPMy/2w8yK/H6uinqeREQFTWZbMpmZmYn/3a9fvzTJZkJCAl6+fImHDx8iJSUFAJCUlJSt/qVSqbgtbsOGDdNt06JFC1y5cgU3b97E77//jlu3bkEQBNja2qZJ0AGgXbt2mS5pLSyynBSrq6ujQoUK4s+2trawsLCQOZafNm/enOn51q1bZ3kjbSIiKvyK0n6aRem5EhEVFNnZkunLly84cuQIrl27hpcvX+LTp0/ibFyJRAIA2a7J9PXrV8TFxQHAL4txvXv3DgDE0eiMtnr6uXBxYSV39emicMeAiIiIiIgoN929exejRo1CdHQ09PT0YGVlhY4dO8Lc3Bx2dnYYMmSImLRmR+oIs5qaGjp27Jhp258HNjNKwFVVVbMdhzKSOykmIiIiIiKirBMEAbNmzUJ0dDSGDx+OP//8M03iGRUVJddj6+npQV1dHVKpFEuXLoWmpuYvr0kdIX779m265+VZ16yMslRoi4iIiIiIiHLm8+fPePXqFQBg/PjxaRLiu3fvIjo6GsD/jfxmlbq6OurWrYuUlBR4eXml22bFihXo2rUrXFxcAHxfe6yqqgpfX19ERESkae/p6ZmtGJQVk2IiIiIiIqJ8ULx4cairqwMALl68KHPuyZMnmDFjhvjzj9vhZtXw4cMBAIsWLcKtW7dkzl24cAF79+7F06dPYWVlBQDQ19dH165dER8fj+nTp4sJOQDcunULu3btynYMyojTp4mIiIiIiPJBsWLFMHDgQOzevRszZszAwYMHYWhoiLdv3+LRo0fQ0tKCsbEx3rx5g/Dw8Gw/fosWLTB27Fhs3rwZgwcPRo0aNWBsbIzQ0FA8efIEADBt2jTUq1dPvGbWrFl4/vw5rl69ijZt2sDW1hZfv36Fr68v6tatCz8/v1x7/gUVk2IiIiIiIso2tWpVFR1CthSUeGfMmAEzMzMcPHgQgYGBePToEcqVK4fevXtjxIgR8PLywrJly+Du7o527dpl+/EnTZoEOzs7uLi44N69ewgMDESZMmXQsmVLDBs2DPb29jLtdXV1sW/fPuzevRunTp2Ct7c3SpcujdGjR6Nnz55o27Ztbj31AitLSfHRo0dRoUKFDPe7IiIiIiKiokOQSpVyj3JBKoUkFysqGxsb49mzZ9m6RkVFBb///jt+//33dM8PHToUQ4cOlTmW3s4/y5cvx/Lly9N9jIYNG2Yrd9PS0sLYsWMxduzYNOey+/yUUZbWFK9fvz7NCz5r1ixs27YtT4IiIiIiIqKCKzcTy/ykrHFT3spSUhwZGZnm2PHjx+Ht7Z3rARERERERERHllyxNny5VqhQCAwPh6uoKW1tbaGhoAAASExOztbG0kZGRfFESERERERER5YEsJcWtW7fGwYMHMW/ePPGYRCLBo0eP0Lp16yx1JJFIEBAQIF+URERERD/J7bWBBbVPIiLKW1lKiqdNm4b379/D29sbgiDI1ZG81xERERGlR6KqiojxE5AcGJQv/alVq6qUhYWIiChzWUqKdXR08O+//yIuLg6RkZFITk5GmzZtYG1tjfXr1+dxiERERETpSw4MQtKjR4oOg4iIlFi29inW0tKClpaW+LOGhgYqVKiQ60ERERERERER5YdsJcU/evr0aW7GQURERETp4NppUgQufaSCKi/em3InxalSUlJw5swZXLp0CS9fvkRMTAx0dHRQuXJlNGvWDF27doW6unpuxEpERERU5HDtNOUnFZXvO7ZKpVIFR0KUvtT3Zup7NTfkKCkOCwvD2LFjERAQkCZjf/78OS5evIgDBw5g48aNnGZNREREJCeunab8oq6uDlVVVcTFxaF48eKKDocojW/fvkFdXT1XB17lTooTEhIwcuRIPH/+HMWKFUObNm1gZWUFHR0dREVF4dGjR/D09ERAQADGjRuHI0eOiPsbExERERH9jFPFFU8ikUBbWxuRkZHQ19eHKl8bKkDi4uIQFRUFPT09SCSSXHtcuZPigwcP4vnz5zA1NcX27dthbGycpk1oaChGjhyJZ8+e4dixY+jXr1+OgiUiIiKiwotTxQsGQ0NDhISE4NWrV9DX14empmauJiBE2SEIAqRSKb59+4aoqChoamrCwMAgV/uQOyk+d+4cJBIJ1q1bl25CDAAmJiZYv349unXrhtOnTzMpJiIiIqJMcaq44mloaMDY2Bjh4eF4//69osMhAvB9ar+enh4MDAxyfQaD3EnxixcvULlyZVhaWmbaztLSEqampggODpa3KyIiIsoGtWpVC2VfRJR/tLW1UbFiRSQnJyM5OVnR4VARp6KiAnV19TybsZCjNcU6OjpZaqutrY13797J2xURERFlkSCV5vt0UK7JJMoeRX1m5OlXTU0Namo53rCGqECT+x1evnx5vHjxQtyCKSMxMTF48eIFDA0N5e2KiIiIskiiqoq9AXsQFhOWL/2V1SmLQTWG5EtfRIVFfq+dBrh+migzcifFDRs2xJEjR7By5UosXLgww3YrVqxAQkICGjZsKG9XRERElA13w+4iOPJFvvRVpaQZk2IiOXDtNFHBIfeOx8OGDYO6ujqOHDmCkSNHwsfHBxEREUhKSkJERAR8fHwwYsQIuLq6Ql1dHcOGDcvNuImIiIiIiIhyTO6R4sqVK2PJkiWYPXs2rl69iqtXr6ZpIwgC1NTUsHjxYpiamuYoUCIiIiIiIqLclqNV8w4ODqhUqRI2bNiA27dvIykp6f8eWE0N9vb2mDBhAurUqZPTOEkJ5XdFUlZAJSIiIiKi7MpxKbnatWtj586diIuLQ2hoKKKjo6Gjo4OKFStCS0srN2IkJaSI6qep/bICKhERERERZVWu1VfX0tKCubl5bj0cKbn8rn4KsAIqERERERFlHzcdozyTn9VPAVZAJSIiIiKi7JO7+jQRERERERGRsmNSTEREREREeUKQSotEn6TcOH2aiIiIiIjyhERVFRHjJyA5MChf+lOrVlUhxV5JuTEpVoD83DqI2xQRERERkSIlBwYh6dEjRYdBlCG5k+KoqCjo6urmZixFgiK2KuI2RUREREREROmTOykeOnQoihUrhs2bN0NPTy8XQyrc8nurIm5TRERERERElDG5k+KXL19CX1+fCbEc8nOrIm5TlD84JZ6IiIiISDnJnRSrq6tDW1s7N2MhUkqcEk9EREREpLzkToodHBywf/9+nD9/Hh06dMjNmIiUCqfEExEREREpL7mT4t9//x0BAQGYMmUK9u/fj3r16sHQ0BCampoZXtOrVy95uyMq0DglnoiIiIhIOcmdFHfr1g0AIAgC7ty5gzt37vzyGibFRMqP66dJWeX3+4nvXyIiIuUgd1Jcvnz53IyDiJQA10+TslLEeze1X75/iYiICja5k2JPT8/cjIOIlADXT5Oyyu/3LsD3LxERkbKQOykmoqKJ66dJWeXnexfg+5eIiEhZ5EpSnJKSgsePHyM4OBjfvn3DwIEDkZSUhA8fPsDExCQ3uiAiIiIiIiqQFLVchst0ckeOk+Jjx45h48aNCAv7vylpAwcOxLt379CpUyd07NgRS5cuzbQqNRERERERkbKSqKoiYvwEJAcG5VufatWqKqReRmGUo6R47dq12L59OwRBgIqKClRUVCCVSgEAHz58gFQqxZkzZxAWFobdu3dDTY2ztYmIiIiIqPBJDgxC0qNHig6D5KAi74U3b97Etm3bUKxYMSxYsAC3b99GrVq1xPP29vZYuXIltLS0cOfOHRw+fDhXAiYiIiIiIiLKLXInxXv37oVEIoGjoyP69u2L4sWLp2nj4OCAlStXQhAEnDp1KkeBEhEREREREeU2uZPie/fuwcDAAB07dsy0XZs2bWBoaIigoPybX09ERERERESUFXInxZGRkShbtmyW2pYtWxbx8fHydkVERERERESUJ+SufKWnp4fQ0NBfthMEAW/evEGpUqXk7QoAcOLECRw9ehRPnz5FXFwcSpcujYYNG2LUqFGoUqVKmvZnz56Fi4sLgoODIZVKYWlpicGDB6N9+/Y5ioOIiIiIiIgKD7lHiuvVq4eoqCicOXMm03bHjx/Hly9fULduXbn6EQQBf/75J2bMmAF/f3+YmZmhWbNmUFVVxfHjx9GjRw/cuHFD5pqVK1diypQpePbsGerVq4fatWvj/v37mDhxIv755x+54iAiIiIiIqLCR+6R4kGDBuHChQtYtGgRihUrhtatW8ucT0lJwbFjx7B06VJIJBL07dtXrn5OnjyJ06dPw9DQEDt37oS5uTkAQCqVYsOGDfj3338xbdo0XLx4Edra2rh+/Tp27tyJChUqYN++fTAyMgIAPH36FEOHDsXmzZvRokUL1K5dW96nTkRFgFq1qoW6PyIiIiL6Tu6k2NbWFiNGjMCOHTswfvx46OjoICkpCQDQq1cvhISEICYmBoIgoHfv3mjUqJFc/Rw9ehQA8Oeff4oJMQCoqqpi8uTJ8PDwQGBgIK5fv442bdrg33//BQBMmTJFTIgBwNLSEpMnT8b8+fOxa9cujhgTUYYEqRT6ThsV0q9EVTXf+yUiIiIqyuROigFg2rRpMDY2xsaNG/H582fx+KP/v2l1iRIlMGrUKIwcOVLuPnR1dWFmZgYbG5s05yQSCUxNTREYGIiPHz8iOjoad+7cgbq6Olq1apWmfbt27bBgwQJcuXIFKSkpUFGRe/Y4ERViElVV7A3Yg7CYsHzrs6xOWQyqMSTf+iMiIiKi73KUFANA37590bNnT/j7+yMwMBDfvn2DlpYWTE1NYWtrCy0trRw9/qZNmzI8J5VK8fjxYwBA+fLl8eLFC0ilUpiYmEBHRydNe319fRgYGODTp094/fo1KleunKPYiKjwuht2F8GRL/KtvyolzZgUExERESlAjpNiAFBXV4ednR3s7Oxy4+Gy7MCBA3j79i1KlSqFBg0awMfHBwAy3SqqTJky+PTpEz59+sSkmIiIiIiIqIjLlaT4/fv38PHxwYsXLxAXF4eSJUvC3NwcjRs3hr6+fm50kcaNGzewcuVKAN/XG2tpaSE2NhYAMh2d1tTUBACxLREREREREWWNImqg5HWfOUqKv337hqVLl+LUqVNISUkB8H0LJYlEAgDQ0NDAkCFDMGnSJKjm4pPw8vLC5MmTkZiYiP79++P3338HgGz1kRovERERERERZY1EVRUR4ycgOTAoX/pTq1Y1zwugyp0UJyYmYsiQIXjy5AkEQYCxsTEsLCygo6ODb9++4cmTJ/jw4QO2b9+OkJAQbNiwIVcC3rt3L5YtWwapVIpBgwZhzpw54rnUdcTx8fEZXp+QkAAA0NbWzpV4iIiIiIiIipLkwCAk/f/iyoWB3Emxi4sLAgICUKZMGaxcuRINGzZM0+bixYuYO3cuLl68CDc3N3Tr1k3uQJOTk7Fo0SIcPnwYEokEf/75J0aNGiXTJnUt8adPnzJ8nI8fPwIADA0N5Y6FiIiIiIiICge59yQ6ffo0JBIJNm3alG5CDABt27bFunXrIAgCDh06JHeQ8fHxGD16NA4fPoxixYph/fr1aRJiAKhatSrU1NQQGhoqjgj/KCIiAp8/f4aWlhYqVqwodzxERERERERUOMidFIeEhMDMzAy1atXKtF2jRo1QqVIlPHv2TK5+pFIpxo0bh6tXr0JfXx979+5Fhw4d0m2rqamJBg0aIDExEV5eXmnOu7u7QxAENGvWLFfXOBMREREREZFykjsp1tTUhIpK1i7X0tKCmpp8M7W3bNmCq1evQltbGy4uLr9MwgcPHgwAWL58OV69eiUef/r0Kf755x8ASHeUmYiIiIiIiIoeudcUN2jQABcvXsSLFy9gZmaWYbt3794hKCgIjRs3znYfkZGR2LlzJ4Dva4C3bt2aYduuXbuiadOmaN68Ofr3748DBw6gS5cuaNCgAaRSKW7duoWkpCT8+eefsLKyynYsREREREREVPjInRRPmTIF165dw7hx47BlyxaYmpqmafPp0ydMmDABqqqqmDx5crb7uH37trifcEhICEJCQjJsa2VlhaZNmwIA5s2bBysrKxw8eBC3b9+GpqYm6tSpg2HDhqF169bZjoOIiIiIiIgKpywlxTNmzEj3uLGxMZ4+fSqOyNasWRM6OjqIiYnBy5cv4ePjg/j4eLRs2RLXrl1D9erVsxVc27Zt5VqLLJFI0LNnT/Ts2TPb1xIREREREVHRkaWk+OTJk5BIJBAEId3zycnJuHr1Kq5duyYe+7Gtp6cnvLy8MGLEiByGS0RERERERJR7spQUd+vWDRKJJK9jISIiIiIiIspXWUqKly9fntdxEBEREVEG1KpVLZR9EREVBHIX2iIiIiKivCdIpdB32pjvfUpUVfO1TyIiRcmVpPjbt2+Ijo7OcM1xKiMjo9zojoiIiKjIkKiqYm/AHoTFhOVLf2V1ymJQjSH50hcRUUGQo6T4yJEj2LZtG96+ffvLthKJBAEBATnpjoiIiKhIuht2F8GRL/KlryolzZgUE1GRIndS7Obmhnnz5mW5/a9GkYmISDG4VpGIiIiKMrmTYhcXFwBA06ZNMWrUKBgaGkJNjUuUiYiUCdcqEhERUVEndxYbFBSEkiVLwsnJCZqamrkZExER5ROuVSQiIqKiTu6kuFixYqhQoQITYiIiJce1ikRUkHBJBxHlN7mTYmtra9y7dw9JSUlQV1fPzZiIiIiIqAjikg4iUgS5k+JRo0Zh6NChWLduHWbMmJGbMRERERFREcQlHUSkCHInxfb29pg/fz4WLVqER48eoVmzZtDX14eKikqG13Tr1k3e7oiIiIioCOCSDiLKb3InxUlJSfD19UVKSgp8fX3h6+ubaXuJRMKkmIiIiIiIiAoUuZPiTZs24cyZMwAAFRUV6Ovrc20xERERERERKRW5k+IzZ85AIpFg7NixGDlyJIoVK5abcRERERERERHlObmT4rCwMJQvXx4TJkzIzXiIiIiIiKgQ4TZbVNDJnRSXKlUKJUqUyM1YiIiIiIioEOE2W6QM5E6KW7RogaNHjyI0NBQmJia5GRMRERERERUC3GaLlIHcSfH48eNx6dIljB07Fo6OjrC2ts7NuIiIiIiIqBDgNltU0MmdFB84cAB2dnY4d+4cevfujVKlSqFcuXLQ0tJKt71EIsG+ffvkDpSIiIiIiIgot8mdFG/ZsgUSiQQAIAgCIiIiEBERkWH71LZEREREREREBUWOpk8TERERKRKr2pKyyu/3E9+/RBljUkxERERKiVVtSVkp4r2b2i/fv0RpyZ0UExERESkSq9qSssrv9y7A9y9RZuROit+9e5fta4yMjOTtjoiIiCgNVrUlZZWf712A71+izMidFLdu3Tpb7SUSCQICAuTtjoiIiIiIqMDiOnHlJXdSLAhCltpJJBIYGhrK2w0REREREVGBxnXiyk3upNjDwyPDc3Fxcfj48SMuXLgAV1dXtG7dGvPmzZO3KyIiIiIiogKL68SVm9xJcYUKFTI9X7VqVTRq1AimpqZYvnw5bGxs0LlzZ3m7IyIiIiIiKrC4Tlx5qeR1BwMGDEDJkiWxb9++vO6KiIiIiIiIKFvyPClWU1NDhQoV8Pz587zuioiIiIiIiChb8jwpTkhIwJs3b/K6GyIiIiIiIqJsk3tNcVZERETA0dERkZGRqF+/fl52RURERERERPkgP7eDyo++5E6KW7RokeE5QRCQmJiIyMhICIIAiUSCfv36ydsVERERERERFQCK2H4qr7eekjsp/vDhQ9Y6UFPDiBEjWHmaiIiIiIhIyeX39lP5sfWU3EnxsmXLMj2vqqqKUqVKoXbt2tDV1ZW3GyIiIiIiIipA8nP7qfzYekrupLh79+65GQcRERERERFRvsvz6tNEREREREREBRWTYiIiIiIiIiqysjR9unr16jnuSCKRICAgIMePQ0RERERERJRbspQUC4KQ13EQERERERER5bssJcUuLi7ZfmB3d3ccOHAAwPek2tbWNtuPQURERERERJSXspQU29nZZfkBv379ikWLFuHcuXMQBAFaWlqYNm0aBgwYIHeQRERERERERHlB7i2Z0nPp0iUsWLAAnz9/FkeHHR0dYWJikpvdEBEREREREeWKXEmKIyMjsWjRIpw9e1YcHf7zzz8xcODA3Hh4IiIiIiIiojyR46TYw8MD8+fP5+gwERERERERKR25k+KoqCgsXrwYp0+f5ugwERERERERKSW5kmIvLy/MmzcP4eHhHB0mIiIiIiIipZWtpPjbt29YsmQJTp48KY4OT506FYMGDcqr+IiIiIiIiIjyTJaTYm9vb/z999/49OkTBEFA/fr14ejoiIoVK+ZlfHJ7+fIlNm3ahLt37+Lz588oV64cOnbsiFGjRkFHR0fR4REREREREVEBkKWkeNasWXBzc4MgCFBRUcGAAQMwePBgSCQShIaGZrmz/Jpe/eDBAwwZMgSxsbGoXbs2rK2t4efnh3///Reenp44cOAASpQokS+xEBERERERUcGVpaT4+PHjkEgkkEgkEAQB+/fvx/79+7PVkUQiQUBAgFxBZkdSUhImT56M2NhYLF++HN27dwcAxMfHY8qUKfD09MSaNWuwYMGCPI+FiIiIiIiICjaVrDYUBCFH/0tJScnL5yE6c+YM3r59i8aNG4sJMQAUK1YMjo6O0NbWxtGjRxEVFZUv8RAREREREVHBlaWRYg8Pj7yOI9d4eXkBANq1a5fmXKlSpWBvbw8vLy9cvXoVnTp1yu/wiIiIiIiIqADJUlJcoUKFvI4j1zx//hwAYGFhke75atWqwcvLC8+ePWNSTEREREREVMRJBEEQFB1EbqpXrx5iYmLg5eUFIyOjNOddXFywdOlS9OjRA8uWLcv24/v5+UEQBGhoaMgdY2RCJJKFZLmvzw41iRpKapbMl75+lp/PEyg6z1WRzxMoOs+V79+8UVSeJ1B0nit/J+WPovI8gaLzXPk7KW8UlecJFJ3nmpPnmZiYCIlEgnr16mXeh1yPXoDFxcUB+L6GOD2px2NjY+V6fIlEIl9gP1DkH5r8VFSeJ8DnWhgVlecJFJ3nWlSeJ8DnWhgVlecJFJ3nWlSeJ1B0nmtReZ6A8jzX1GLRv1LokmJVVdUsFfWSd4C8bt26cl1HREREREREBU+Wq08rCx0dHQBAQkJCuufj4+MBANra2vkWExERERERERVMhS4pNjQ0BAB8+vQp3fMfP36UaUdERERERERFV6FLilOrTgcGBqZ7PigoSKYdERERERERFV2FLilu0aIFAODChQtpzn358gW3bt2CpqYmGjZsmM+RERERERERUUFT6JLiNm3aoEKFCrh8+TIOHTokHo+Pj8ecOXMQGxuL3r17Q19fX4FREhERERERUUFQ6PYpBgBfX1+MGDEC8fHxqFmzJoyNjeHv74+PHz/CysoKLi4uYkEuIiIiIiIiKroKZVIMAM+fP4eTkxNu376N2NhYGBsbo2PHjhg2bBiKFy+u6PCIiIiIiIioACi0STERERERERHRrxS6NcVEREREREREWcWkmIiIiIiIiIosJsVERERERERUZDEpJiIiIiIioiKLSTEREREREREVWUyKiYgoXePGjcOaNWsUHQYRUZEUERGh6BCIigw1RQdAstzc3GBiYgIbG5tM23l4eODhw4eYPHly/gSWQy4uLjm6fvDgwbkUCRFl1Y0bN/DlyxdFh5Evevfujd9//x2dO3eGtra2osMhIsKgQYNQqlQp7Nu3T9GhEBV6TIoLmJkzZ6Jr166/TIpPnDgBHx8fpUmKHR0dIZFIsn2dIAiQSCRKlRR3795d7mslEgn++++/XIwmb/FmR8Y+ffqEsLAwVKlSRWmTLFVVVejq6io6jHzx4MEDPHz4EMuWLUOnTp3Qq1cv1KlTR9FhUR4qDJ9RkvXgwQO8f/8eNWvWhLGxsaLDybHQ0FCYmJgoOox8sXjxYpiZmaF///6KDoVy2YMHD1CuXDkYGhoCAAICArB+/Xq8f/8etWrVwoQJE1CuXDkFR8mkWOFOnToFqVQqc+z169dwc3PL8JrIyEjcuHEDamrK88/XrVu3NEmxv78/QkJCULp0aTRr1gwmJiZQU1NDWFgYvL298ebNG9StWxfNmjVTUNTyefLkSbrHJRIJBEHI9Jw8Nw4UqSjd7MjI69evsXv3bjg4OKBu3boAgH/++Qfbtm1DSkoKihcvjlmzZqFHjx4KjjT7evXqhX379uH69eto1KiRosPJU66urnB1dcXZs2dx9OhRHDt2DFWrVkWvXr3g4OCAUqVKKTrEXCeVSnHhwgXUrFkTFStWBAB4e3tj5cqVePfuHWrXro1Zs2bBwsJCwZHmTGH+jGYkKCgIT548QVRUVJrvGD9S1t/B/v7+2LJlC4YMGYLGjRsDAGbMmIFTp04BAFRUVPDHH39g/Pjxigwzx0qXLo3IyEhFh5EvTp48CRMTkyKbFD958gSHDh3C+/fvUb58eXTr1k38faWsEhMTMXHiRHh7e2P58uXo2rUrPn/+jCFDhiA6OhqCICAwMBDXr1/H8ePHoaenp9B4lSerKqT8/f1x8OBB8WeJRIJ79+7h3r17mV4nCAJ+++23PI4u9yxfvlzmZz8/P5w6dQpdu3bFokWLoKmpKXN+9uzZcHR0xMGDBzFmzJj8DDXHfh49lUqlcHR0xMuXL9G3b1+0b98eJiYmUFVVRVhYGDw8PLB7927UrFkzzetU0BWlmx3pCQ0NRa9evfDt2zdUq1YNdevWxd27d7FlyxYAgJGRET58+IA5c+bA1NRU6f7AWVhYwNjYGMOHD4eZmRmqV68OPT09qKikLUchkUgwc+ZMBUSZO6ytrWFtbY05c+bg3LlzOHbsGO7cuYPly5djzZo1aN26NXr16iV+AVd20dHRGDx4MJ48eYKlS5eiYsWKCA0Nxfjx45GUlAQAuHnzJgYNGoQTJ06gfPnyCo5YPoX9M/qzH7+EZkaZb0w+ffoUQ4YMQVJSEpo2bYrGjRvD29sbJ0+ehJqaGurWrYuAgABs2rQJ1tbWaN68uaJDltukSZMwa9YsrFu3DkOGDIG+vr6iQ8ozycnJMDIyUnQYeSYhIQHbtm2Du7s7YmJiYGNjgwkTJqBSpUo4c+YMpk+fDkEQxMGTI0eOYNSoUZgyZYqCI5ffgQMHcPnyZRgYGIg3lg8dOoRv376hfv36mDhxIk6dOgVXV1fs2LED06ZNU2i8TIoVbNKkSfj06RNSUlIAfF8rXL58edSoUSPd9hKJBJqamqhSpQqGDh2aj5Hmrg0bNqBMmTJYsmQJ1NXV05xXVVXFnDlzcOXKFWzZskWp/qjZ2dnJ/Lx7924EBQVh06ZNaNWqlcy5MmXKwMrKCg0aNMDQoUPx33//YdKkSfkZbo4UpZsd6dmxYweioqLg4OAg/tseO3YMEokEY8aMwaRJk3Dr1i0MHToUzs7OSveFe+bMmeIshqCgIAQFBWXYVtmT4lSampro1q0bunXrhtDQUBw9ehTnz5/HuXPncP78eRgZGaFnz57o2bMnypYtq+hw5ebs7IyAgABUrVoVlStXBgAcPnwYSUlJaNOmDWbNmgU3Nzds3LgR27dvx7x58xQbsJwK+2f0Z//++y8uX74MFRUV1KtXD+XLl1eqWWVZsWvXLiQmJmL06NHi6P6JEycgkUgwY8YMDB48GIGBgejevTsOHDigVN8ffnb37l1UqlQJ27Ztw7Zt22BoaJjpjUllWn71s/bt28Pd3R1BQUGoWrWqosPJVYmJiRg4cCAePXokJr2nT5+Gj4+PzO/X5s2bo1q1anj58iU8PT2xbds2NGjQAA0bNlRk+HI7d+4ctLS0cPToUXF69KVLlyCRSDBt2jTUqVMHtra2uH79Ojw9PRWeFEuEjOZzkkJYWlrCwcEBK1euVHQoeapu3bpo3rw51q9fn2m7yZMnw9vbG/7+/vkTWB5o3749SpUqhUOHDmXabtCgQXj79i08PT3zKbLcN3ToULx69QoXLlxI92YHAKSkpKB9+/YoXbr0L1+Tgq5t27YAgAsXLogj5o0bN0ZERAQuX74sJk39+vVDaGgorl69qrBY5bFx48ZsTY9X9qmKGYmMjMSWLVvg4uIi3sBUU1NDu3btMHnyZHHqsTLp3r073r17h0uXLqFEiRIAgA4dOuDVq1dwc3MTp0x37twZiYmJuHjxoiLDlVth/4z+rH379nj79i327t2r9Al+Rlq2bInixYuLU6VTUlJgb2+PmJgYXLt2TRyRGjJkCJ4/f44bN24oMtwcsbS0zHJbiUSS4fItZXDt2jUsWbIEb968QaNGjTKdmQQo19T/HTt2YPXq1ahTpw4mTJiA8uXL48GDB1i2bBmSkpIQFxeHpUuXyizhOHv2LKZOnYrffvsNq1evVmD08rO1tUW9evWwdetWAEB4eDiaNGmCkiVL4tatW2K78ePH4+rVq7+cJZvXCtftw0LAw8NDLPiRlJQkk1hERkYiMjJSKb+A/UxLSwsfPnz4ZbuQkBAUL148HyLKOx8+fED16tV/2a5UqVJ4+PBhPkSUd+7fv4/mzZtnmBAD39d61axZ85fT+5TBx48f0bx5c/HL9pMnT/D582eYmprKjCIaGhoq5b/thAkTFB2CwiQmJuLSpUs4fvw4bty4AalUColEgqZNm8LGxgZnzpzB2bNn4eXlhR07dvyyOGJB8+rVK9jb24sJ8bt37xASEgIDAwOZNcRmZma4fPmygqLMucL+Gf3Z+/fvYWtrW2gTYuD7F+vatWuLPz948ADfvn2DpaWlzNp/PT09fPv2TREh5pqcFrNUJsOHDxdnJnl7e+PKlSvptlPGqf8XL15EyZIlsXPnTujo6AAAqlSpAhUVFcyYMQNGRkZpahp06tQJmzdvVurfS8nJyTIzBq9duwYAqF+/vky7xMTEfI0rI0yKC5gKFSrg7du3mDlzJpKTk7Fz507x3PXr1zF16lTY2dlhyZIlSl2RsFatWvD29salS5fQpk2bdNscPnwYT58+Vaq10+kpW7Ys7t27l+Ymx49iYmLg6+uLChUq5HN0uaso3ewAAB0dHcTHx4s/+/j4AADs7e1l2oWFhYl/CJWdVCpN84euMHnw4AH+++8/nD17Ft++fYMgCChbtix69uyJXr16iWvexowZg23btmHt2rVYunSp0k1blEgkMiMwqSOkPy//iImJgaqqar7GlpuK2mdUV1cXWlpaig4jT+np6ckUn0pNnho0aCDTLjQ0VOmr5//8eSzM0qtRUli8evUKVlZWaX7HNGnSBABgbm6e7nVmZmYZ3hxQBhUqVMCzZ8/En728vCCRSGRqc0RHR+P+/fsFolo8k+ICJiwsDH369EF4eDiqVKkic05dXR16enq4desWBg4ciOPHjytt0YXRo0fj6tWrmDx5Mrp164bmzZujXLlyEAQBb9++hbu7O9zd3aGtrY0//vhD0eHmSNu2bbFz507MmjULixcvTvOF5evXr5g2bRq+fv2KIUOGKCjK3FGUbnYAQOXKlXHnzh18/vwZurq6OH36NCQSicwaNn9/f9y/f1+pv9w8ffoUu3btws2bN/Hp0yc4ODhgxYoVmD17NkxNTfG///1PqRMnANi+fTvc3NwQHBwMQRCgqqqKFi1aoHfv3mjevHm6U/hGjhwJJycnvHjxQgER54yJiQkePXqElJQUqKio4OLFi+JIeKrw8HD4+fnB1NRUgZHmTFH5jKZq2rQpLl68iOjo6EJx4zE9ZmZmuHPnDoKDg2FoaIiTJ09CIpGgRYsWYpsLFy4gICBAqdcTp+fVq1d49+4dSpYsiRo1aiAqKkrpE/9UylZoNDuioqLSraxcsmRJAMjwhpyGhobMTT1lY29vjwMHDuCvv/5ChQoVcOHCBaiqqorLWvz9/bFmzRpERUWhT58+Co6WSXGBs3XrVoSHh6N///6YMWOGzLk2bdqgZcuWWLZsGfbt24fNmzdj7ty5Coo0Z+rWrYsVK1Zg/vz54vYnPxIEAaVKlcLq1athZmamoChzx8iRI3Hp0iWcPn0a3t7eqF+/PsqXLy/eAPD19UVcXBysra0xbNgwRYebI0XpZgcAODg4wM/PD7/99htKlCiB169fo1y5cmJisXDhQnF7td69eyswUvm5urpi4cKFSE5OFo+llqLw9/fH8ePHcf/+fWzYsCHDtV/KYM2aNQC+VyPu1atXlgppJSUlQSKRKOV+xs2bN8fWrVsxbNgwlCtXDj4+PtDS0kLr1q0BAGfOnIGTkxPi4+PRoUMHBUcrv6LwGf3RhAkTcPnyZYwfPx7Lli1T2qrhmenTpw9u3rwJBwcHaGpqIiYmBmZmZuJI8ZgxY+Dj4wOJRIJBgwYpONrcsX//fuzYsUOcidWlSxesXLkS48ePh6amJhwdHVGmTBkFR0kZSUlJSffGceoxZb+pnJE//vgDHh4eOHHihHhs5MiR4nt1woQJCA8PR40aNTBy5EhFhSliUlzAXL9+HZUqVcLff/+d7jQSVVVVzJ49G97e3vD09FTapBj4XsDF3t4ex48fx82bN/Hx40cA36cbN2nSBF27di0Ue4Pq6elh7969WLp0KS5cuAAvLy+Z86qqqujRowdmzZql9FNSi9LNDgDo27cvnjx5giNHjuDLly/ic0ut9nrjxg3Ex8dj8uTJ6Ny5s4Kjzb579+5h/vz50NbWxpgxY9CiRQuZEf4pU6ZgyZIl8PDwEKuOK6t27dqhd+/eaNy4cZan8GloaOD+/ft5HFneGDlyJLy9vWWKnUybNk0cdVq1ahU+fPiAZs2aKfVOB4X9M9q9e/c0x1RVVXHr1i20atUK5cqVg66ubqGqVtyxY0eEhoZiy5YtiImJQbVq1WSKdr558wYaGhpYtGiROD1Vmc2cORMnTpyAIAjQ19dHRESEeO7Dhw94/fo1Bg4ciKNHj4o1ApTZixcv4OzsLH4v1NDQQOnSpdGwYUP07dtX6fdNL0oMDAxw/PhxHDp0CB8/foS9vb3MTdYWLVqgXLlyGD58eIFY9sHq0wVM7dq10aJFC/zzzz+Ztps4cSI8PT3x6NGjfIqMckN4eDhu3bqFsLAwAEC5cuXQoEEDpZ0Gn5Hw8PBCf7PjR2FhYQgLC4OFhYXMjQ1PT09YWFgo7VrxcePGwcvLCy4uLmJhjJ8r5L948QIODg6oW7cu9u3bp8hwc2Ts2LGoUqWKwreEyE+JiYlwd3dHWFgY7OzsUKtWLfGck5MTjI2N4eDgoNQzAFIV1s9odqoT/0zZqxUnJiYiOjo6zd/PBw8eoFq1agXiS3ZOnThxAn/99ReqVasGR0dHWFtby/wO/vDhA2bMmAFfX19MmjRJ6bc6PHv2LGbNmoXExESkl55oaGhgyZIlcHBwUEB08stsZ5nMzk2fPh2nT59W6s+pMuFIcQGjp6eHd+/e/bJdeHh4obgjCHyvlnn79m3xjqCBgQHs7OwK5VQgAwMDpRyNyC4DAwOMHDmyQEyHyQ9ly5ZNd6rtz/tSKxs/Pz/UqVMnTaXIH5mZmcHGxgbBwcH5GFnuu3nzJr5+/aroMPKVhoYGunTpku65H7fXCgsLU+o9mYHC+xktStWJf6ahoZHuDeUfb+4ouyNHjkBLSws7duxI9/1brlw5bN68GS1btoS7u7tSJ8XPnz/HX3/9BalUigEDBqBLly4wMTGBVCrF69evcfr0aRw+fBh///03atSooXR7GT979gxOTk7ZOvdjkSplFxMTg/v37+Pdu3coX748GjdujJcvXxaomhVMiguYevXq4fz58/Dx8ZEpePKj27dvw9/fX6aohDKKjY3FwoULcfr0aXHvz1QqKiro0KEDFi5cqHTFQp4+fQrge7Kgrq4u/pxVObnzT4qRmJiIkydP4ubNm+KWKJMnT8a+fftgZWWllGtOge9VIQ0MDH7ZrkSJEoiKisqHiPKOqqpqoSlYkxUHDx5Ev379ftnO1dUVK1euhK+vbz5ElXcK62e0MBQHk9fnz58RGBiIuLi4NN8hfpS6Tl4ZPX36FPXr18/0plTx4sVRr149+Pv752NkuW/79u1ITk7G8uXL0yzFKVOmDGxsbFC7dm3MnDkTLi4uWLRokYIilc/z58/x/PnzdM89e/Ys3XOp208ps6SkJKxduxaHDh0Si4Z16dIFjRs3xpw5cxAVFYV//vmnQCypY1JcwAwZMgQXLlzA+PHjMWzYMLRp00bcAuTDhw/w9PTEzp07IZFI8L///U/B0covKSkJI0eOxN27d6GhoYGGDRuKdwRDQ0Ph6+uLs2fP4v3799i3b59STd/r1q0bVFRUcObMGZiammZrmwGJRIKAgIA8jjD3dO/eHRKJBE5OTjAyMkp3fVtGlHU9288ePnyIiRMn4sOHD+IfsNSpmEePHsXSpUsxffp0pfy8GhoaZulO9bNnz5R+ZkevXr2wb98+XL9+HY0aNVJ0OHlu8eLFUFNTw++//57u+ffv32Pu3Lm4fv16PkeW+wrzZ/RXXrx4gY8fP0JVVRVGRkYFYtuTnJJKpVi4cCGOHj2a7hTbnynz1FOpVJqlIkxSqRRJSUn5EFHeuXXrFqpVq5ZpbYpu3bph165d4n63yiI7340Kk+TkZIwePRo3btyAmpoarKysZPZdjo6ORlBQEAYPHgw3NzeFf49gUlzA1KlTBzNnzsSKFSuwdetWbN26NU0biUSCGTNmwNbWVgER5o5Dhw7h7t27qFevHtavXw9DQ0OZ82FhYZg0aRL8/f3h6upaIEq1Z1XqTYzUQi6pPxdGT548gUQiQUJCgvhzVin73U/ge+IwYsQIREZGolmzZmjRooXM3euGDRsiKCgIq1atQq1atTKdhlwQNWnSBEeOHMGhQ4fQt2/fdNscOHAAb968UfrKvRYWFjA2Nsbw4cNhZmaG6tWrQ09PL8MCRTNnzlRAlLlHS0sL8+fPh5qaWpovbIcPH8aqVavEbX2mTp2qoChzrrB/RtOTlJSE3bt3Y//+/WJNh1SmpqYYPnw4evbsqaDocm7Pnj04cuQIAMDY2BiGhoaFtnpvxYoV8fDhQ8THx6NYsWLptomNjcWjR49gYmKSz9HlroiICNjY2PyynZmZGTw8PPIhotyzbNkyRYegEIcOHcL169dhb2+PlStXomzZsjKzIQ8fPoy5c+fizJkzcHZ2xvTp0xUYLZPiAmnQoEGwtbXFgQMHxLW2ycnJ4vSRgQMHKv2amZMnT0JHRwebNm1Kt+hS2bJlsXnzZrRp0wZubm5KlRR7enpm+nNhkrqeLTXxL2rr2/79919ERkZizpw54tYfP37h/uuvv1C3bl1MnDgRu3fvVrov3KNHj8aZM2ewcOFC+Pn5oWHDhgCAyMhI3LhxAx4eHjh06BC0tLQwfPhwBUebMzNnzoREIoEgCAgKCkJQUFCGbQtDUrxr1y4MHz4cc+fOhaqqKhwcHPDu3TvMmTMHN2/ehCAIaNWqFebPn6/U64kL+2f0Z6mzsG7dugVBEGBoaAgjIyMIgoA3b94gODgYc+fOxd27d+Ho6KjocOVy/PhxqKioYMOGDWjTpo2iw8lTHTp0wIYNG7Bo0SIsWrRIvNn+I0dHR0RGRmLAgAEKiDD36OrqZqmmzrt375RuWV1Rdfz4cejq6sLJySndOkhaWlpYtmwZbt68iStXrjAppvRZWloq3XqJ7Hjx4gXs7OwyrUKsr68POzs7+Pn55WNklB0/r2crauvbfHx8YGZmlulemO3atUP16tWVcgqfkZERtmzZgokTJ+LkyZM4deoUJBIJrly5gitXrkAQBOjo6GDt2rWoVKmSosPNkXHjxhWK2QtZVbt2bTg7O2P48OGYNWsW/P39cfLkScTExMDAwABz585V6v2JUxX2z+jPXFxccPPmTVSrVg1Lly5NcwP9zp07mDNnDo4fP47GjRsrZeHHV69ewcbGptAnxAAwdOhQnDlzBsePH8edO3fEkdQXL15g/fr18PLywrNnz2BsbKzUW6cB37d09PT0xI0bN8QbsD+7ceMG7t+/r3QF8saPHw87OzsMHjxY0aHkq+DgYDRs2DDTwsAaGhqoXbs2bt68mY+RpY9JcQGXkJCAjx8/Ql1dHeXKlUNKSopSra/NiFQqTfeO58/U1NSQmJiYDxHlveTkZJw7dy7N3nsNGjRA69atlX6P4p8lJibiwYMHMlXFa9asCXV1dUWHlms+ffqUpT/OJiYmePHiRT5ElPtsbW3h7u6OI0eOiEWKUlJSUKZMGdjZ2aFPnz4KXweUGyZMmKDoEPKdlZUV9uzZg6FDh+LQoUOQSCT4/fffMWPGjEKzu0FR+Iz+yM3NDcWLF8fu3bvTLZJXv359ODs7o1OnTti/f79SJsU6OjpFZqRQW1tbnFZ68+ZNvH79GgDw+PFjPH78GABgbW2NtWvXKv1ndsiQIfD09MS4ceMwceJE/Pbbb+J7ODw8HKdPn8bGjRshkUgwZMgQBUebPZcuXYK2tna655ycnGBpaVkob/L8uLwuMzExMQXipjST4gLq8uXL2L59O+7fvw+pVAoHBwesWLEC48aNQ9myZTFjxowMP2DKoGLFirhz5w7i4uIy3EswLi4Od+7cUfp1MgAQFBSEcePG4fXr12kKg7i6usLExAQbNmwoFJWnU1JS4OTkhD179iA2NlbmXIkSJdC3b19MmjSpUKwBy+p0rzdv3ij1FxZdXV2MGDECI0aMUHQolMssLS2xd+9eDB06FJGRkb+8q69sispnNFVoaCgaN26cadX48uXLw97eXmkritva2sLPzw8JCQmF7mZyesqUKQNnZ2c8fPgQN27ckLkxaW9vr9T1ZX5kZ2eHiRMn4p9//sGKFSuwYsUK8d83NbESBAETJ06Evb29IkPNVU5OTnBwcCiUSbGZmRkePnyIyMhIlCxZMt02ERERePToEapUqZLP0aXFpLgA2rBhA7Zs2QJBEKCiogJBEMREKigoCJcvX8bTp0/h4uICDQ0NBUcrn/bt28PJyQl//fUXVq1aleYPW2JiIv766y9ERkZi4MCBCooyd0REROB///sfPn78CEtLS3Tq1AkmJiZISUnB69evcfbsWQQGBmLMmDFwc3ODnp6eokPOkcmTJ+PixYsQBAGmpqYyVcVfv36N7du3Izg4OMP9+pRJ6nQvf39/1K1bN902t2/fRkBAgFJvC1JUSKVSXL9+HW/fvkVSUpLMDayUlBQkJibi06dP8PDwULpaAb8q9FKjRg34+PhgxowZuH37tszvZGVeQ13UPqMlSpRAdHT0L9sJgqC0CeXEiRPRq1cvLF68GPPmzVPa70HZZW1tDWtra0WHkaf++OMPWFlZYefOnfDz8xO38NHQ0EC9evXwv//9D82aNVNwlJRVDg4OWLp0KWbMmIFVq1al2fYwISEBs2fPRmxsLDp16qSgKP8Pk+ICxtvbG5s3b0bZsmUxa9YsNG3aVKYa39q1azF79mzcv38frq6uSltYYdiwYXBzc8PFixfRunVrtG7dWhwRDg0NhYeHBz5//gwTExOlXyezY8cOfPz4EQMGDMDcuXPTTBEZM2YMFi1ahIMHD8LFxQUTJ05UUKQ55+bmhgsXLqBy5cpYs2YNatasKXP+wYMHmDFjBjw8PHD27NkC8UswJ4YNGwYPDw+MGTMG06dPl1kHlZiYiMuXL2PRokWQSCSZrmksqLKy/kkikUBNTQ0lSpRApUqV0LZtW1hZWeVDdLkrKioKgwcP/uUWVMq6b+SePXvEQmKZSU5OxqFDh2SOKXNSXNg/oz9r3749Dh48iHv37mW493JoaChu3ryZ6dY3BdmlS5fQoEEDHDt2DOfPn0f16tWhq6ub7udSIpFg48aNCoiS5NW0aVM0bdoUKSkp+Pr1KwRBgJ6eXqGYXVbU9O3bF+fOnYO3tzdatWqF6tWrAwAePXqE6dOn4/r16/j8+TNq1KhRIPIZiZCVTd4o3wwfPhy3b9+Gm5ubuJG1paUlHBwcsHLlSgDAx48f0bZtW1hYWIjbEiijDx8+YPLkybh37x6A/9uiJ/UtWbt2baxdu1bcT1JZdejQAXFxcfDw8MhwHXVSUhJat26NkiVL4tSpU/kcYe4ZNGgQHjx4gDNnzmS4H2ZoaCg6d+6M+vXrY9euXfkcYe5zdnbGihUrZI6pqqpCEASkpKRAEASMGzdOKdespk7nzyyZ+vmciooKJk+ejFGjRuVLjLll9erV2LFjB4oXL44GDRrgzZs3eP78Obp06YKoqCj4+fkhMjIS1apVw6pVq5RuqUNOZ2aMHz8+lyLJf4X5M/qz2NhY/O9//8OLFy8wZcoU/Pbbb+LoTGJiIq5cuYJly5ZBEAQ4OztDX19f5nplWKtraWmZpRs8wPffT8peQO3evXvYvXs3nj59iqioKEil0nTbSSQS3Lp1K5+jyz2vX79GxYoVf9nu8+fPWLhwITZs2JAPUeWOn7/HZ/VcYRAXF4clS5bgxIkTSE5OljknkUjQrl07LFq0KMPp1fmJI8UFzMOHD2FjYyMmxOkxNDRE/fr1xSILyqpcuXLifsWpW0+lbiFhZ2en9FtjpHr37h1atGiRaWExdXV11KlTBz4+PvkYWe57+vQp6tevn2FCDHwvaGNra4uAgIB8jCzvDB06FDVr1sT27dvh6+uLuLg4JCcnQ11dHTY2Nhg+fDiaN2+u6DDlcunSJUybNg33799H9+7d0alTJxgbG0MQBLx//x7u7u44duwYTExMMHnyZLx58wbOzs5Yv3496tSpo1TVyC9fvgw1NTW4urrC1NQU58+fx5QpUzBo0CBYWVkhJiYGEydOxPXr1/Ht2zdFh5ttypzU5lRh/oz+rFOnTkhOTsa3b9+wePFiLF68WBxl+/r1q0xC1b59e5lrJRKJUvxeLkp7vvr7+2PQoEGQSqVZugmgzAYPHox9+/Zl+v3h9OnTWLJkCSIjI/MxMsoJLS0tLF26FJMnT4avry/evXuHlJQUGBoawtbWtkANfDEpLmDi4+Oho6Pzy3bq6uqIi4vLh4jyno2NTZY2bFdWGhoaiImJ+WW7mJgYpZ8elJCQkKX3r46OTpZeE2Vha2sLW1tbcbpXSkoK9PT0slRhvSA7f/487t+/j6VLl6JHjx4y56pUqYLGjRujUaNGmDx5MsLDwzFixAi0bNkSDg4OOHDggFIlxW/fvkWdOnVgamoKAKhZsyYEQcC9e/dgZWUFHR0drFq1Ci1atICLi0uhKW7z8OFDBAcHy0yl9ff3x4EDB9CnT59Cc3OysH5Gf/bhwwfxv1OTqC9fvmTpWmVJurp3767oEPLN5s2bkZycjBYtWmDo0KEwMjJS+u8JGfnw4QOGDBmCvXv3wsjISOZceHg45s+fD09PTwiCkOHSACpY3r17B21tbejp6aFMmTIZLpkLDQ1FcHCwwm9OFq6/BoWAkZERAgICMt16KTk5GQEBAShfvnw+R5dzz549w4ULFxAeHo4KFSqII0+Fmbm5Oe7cuYO3b99meEfs7du38PX1Vcq1mD8yMjLC/fv3kZycnOGXzeTkZNy/f18p37+/oqKiIk5HlEql+PLlS6Z7cRd0R48eRfXq1dMkxD/q0KEDrK2tcfDgQQwcOBBmZmaoXbu2uCxCWSQnJ8tsLVWhQgWoqakhKChIPKavr4+6devi/v37iggx161atQq7du2Cubm5TFIcGBiIU6dO4fTp0xgxYgT+/PNPBUaZMz9vd/LjZ/RHrq6uuHv3LpYvX57fIeaqp0+fKjqEfPfhwweZbQ4LwxZxqR48eAATExNs2rSp0CbDqYYPH46dO3diyJAh2LdvH8qWLQsAOHHiBBwdHREZGQltbW1MmTJF6QuwFhWtW7cWd8/JzKpVq3Dz5k3cvn07nyJLH5PiAqZVq1bYvXs3NmzYgMmTJ6fbZtOmTfj06ZPS7dPm5OSEzZs3y9yN3rhxI+bNm4fff/9dgZHlrV69esHPzw8jR47EmjVrxEIDqZ48eYLp06cjKSkp0+RDGbRs2RLOzs5Yvnw55s6dm26b5cuX4+PHj0r3/s1IVFQUjhw5giZNmojrTF1dXbFixQrExMTAxMQECxYsQKNGjRQcafa9e/cuS3u8Ghsbw8PDQ/zZ0NAQjx49ysvQcp2+vj4+f/4s/qyiogIjIyOZpBgA9PT0sjzyVpCdP38eO3fuRMmSJeHg4CBzrmXLlpg1axY2b96MHTt2oFatWmjbtq2CIs2ZrG534u3tDR8fH6VPiouSY8eOYdu2beLevanMzMwwaNAg9OnTR0GR5Z6EhARUr1690CfEADB9+nSoq6vj33//xeDBg7Fq1Sps3rwZ3t7eEAQBLVq0wPz585X2hvr169czLF6Z2TmJRII9e/bkZWi55uft7wRBQGxsbKbb4kVFRSEgIACJiYl5Hd4vMSkuYEaMGIHTp09j69atuHPnDho0aAAAeP/+PVxdXeHh4QFvb2/o6elh+PDhCo42665cuSIWeqlevToqVaqEwMBAvHjxAgsWLIClpWWh3WqgR48ecHd3h7e3N3r06IHKlSvLVNoOCQmBIAho1qwZevbsqeBoc2bEiBE4fvw49u/fDz8/P3To0EHmuZ47dw5Pnz6Fvr5+odjz9vPnz+jVqxc+fPgAXV1dWFpa4tmzZ5g/fz5SUlKgqqqK169fY/To0Th+/DiqVq2q6JCzxcDAIEsjT0+ePJHZaiE6OjrN1gsFnbW1NS5fvoyXL1+KU6jNzMxw48YNREdHiwWIAgMDC0RBkJzau3cvNDQ0cODAgTQ1LMqUKYMhQ4agSZMm6NatG1xcXJQmKd6xY4e4jUuqZ8+eZVpoLCoqCj4+PtDW1s7r8AqMqKgouLu7K+0NaUdHR+zduxeCIEBLSwtGRkaQSqV4//49goKCsGDBAjx79gzz5s1TdKg5UrVqVbx580bRYeSbyZMnQ01NDU5OTuJNDX19fcyZM0fpd6sIDw9HeHh4ts8p024Hf//9N65fvy7+LJFIcOnSJVy6dOmX1xaEZZRMigsYfX197Nq1CxMmTMCdO3dw9+5dAMCdO3dw584dCIKA8uXLY8OGDUo1RejIkSOQSCT4+++/0b9/f/G4k5MTnJyccPjw4UKbFAPfn+eGDRuwb98+vHz5Ei9fvhTPaWlpYcCAAZg0aZJS/fJLT+nSpeHs7IyxY8ciICAgTdVPQRBgZGSEjRs3wsDAQEFR5p4dO3bg/fv3sLOzE/dAPXLkCFJSUtC7d2/Mnz8fp06dwsyZM7Fjxw6lG4Vq3Lgxjh49CicnpwwLNW3atAkhISHo0qULgO8Jsb+/PywsLPIz1Bz7/fffcenSJfTt2xfjxo3D4MGD0a5dO3h5eWHSpEkYPHgwPDw8EBwcrJSj/j8LDg6GnZ1dpkUdzczMYGNjgwcPHuRjZDkTGxuLzZs3i79LJRIJnj9/jufPn2d4TersJWVNEH929uxZ7NmzJ8P9thMSEsQqsMr4nL28vODi4gIDAwPMnz8frVu3FpebJScn4+LFi1iyZAkOHjyIVq1aoUmTJgqOWH79+vXD3LlzcfHiRaW5MZVT48ePh4aGBtauXQs1NTU4OzujWrVqig4rR4pKcbhZs2ZhwIABSElJAfD9+4Cqqiq0tLTSbS+RSKCpqYkqVaoUiBtY3JKpgJJKpbh48SJu3LiB9+/fIyUlBWXKlIG9vT06deqkdJvVt2zZEiVKlMDJkydljguCgObNm0NXVxenT59WUHR5SyqVilOfEhMT8fDhQ5lK29bW1tDU1FRwlLkrMTER58+fT7eqeMeOHZXu/ZuRzp0749u3b/Dw8IC6ujqA70sgUiszp24v0aNHD0RERODy5csKjDb73rx5g549eyIqKgp16tRBu3btxBGZd+/e4dKlS7h//z60tbXx33//wdDQEL///jtevHiBefPmoV+/fop+CtmyevVq7Ny5Ex06dMC6deuQlJSErl27Ijg4WNwCRkVFBXv27FH6Qlv16tVDw4YNsWnTpkzbTZw4EVeuXFGaNeLx8fFYv369+KXMxcUFZmZmaNy4cbrtJRIJihUrBlNTU3Tp0kXpp6n6+Phg5MiRv2ynra2dpX//gmjEiBG4efMmXF1d0yxHSvX48WP07t0bzZs3x+bNm/M5Qvn9uAwl1bp16xASEgIHBwfY2NigZMmSGd5Ab926dV6HmGt+NQtpz549OH78OKpUqYLly5en+d6gbNviFUXKtt0Uk2LKF7Vr10aLFi3wzz//pDk3ZswY+Pr6iqPihU2fPn1gbGyMNWvWKDqUPLdt2zZYWFgovIJgfqlbty6aNGmCjRs3Avg++tapUycYGRnB09NTbDdp0iR4enri4cOHigpVbo8ePcJff/2FFy9epPkiJggCKlasiNWrV6NWrVp48eIFOnfuDBsbG+zevVspb348e/YMX79+hb29PQAgIiICa9asgb+/P0qVKoX//e9/SvXFMyM9evTA27dv4eXlleG04YSEBLRq1Qr6+vpKu3+6sn0py6lRo0bhypUr6N+/P3r37g13d3ds3boVR44cgYqKCry9vfHvv//CwMAAJ0+ezNJuAQWNvb09zM3NsXfv3kzbDRo0CC9fvsTVq1fzKbKcS92D+WeCIGRpJpky7cmc0XP9WXrPXVm2Dyvqjh8/DhMTE6XZxYDTpylfJCYmZvgFuXjx4oVme6n0PHv2TCm/eMhj165d0NfXLzJJsYaGBpKSksSfU798pSZUqSIiIpR2NoCVlRVOnDiBK1eu4Nq1a3j79i2Sk5NRvnx5NGrUCG3atBFHyUuVKgVnZ2fY2dllWD2/oPt52re+vj6WLl2qoGjyTseOHbFmzRpMnToVq1atQokSJWTOx8bGYtasWYiIiFC6Ef8fFbVqzI8ePYKRkRHmzp0LFRUVxMXFYcuWLXj//j3atm2LGjVqoFKlSpg6dSqcnZ0xbtw4RYecbTExMShduvQv25UuXVppZjikUvYZKNnx87ZLhVlOfw8p66j4j9unJSYm4sGDB2KleAMDA9SsWVP8/lAQMClWsJzstyeRSPDff//lYjR5J7O7nKnTEgurolS8JS4uTumKSeVExYoVce/ePcTFxUFLSwvnzp2DRCJBs2bNxDYhISHw9/dX6u221NTU0KpVq19WotbX1xeLA1LBNmjQIJw8eRKXL19G8+bNYWdnh/Lly0MikeD9+/fw9fVFdHQ0TE1NMWzYMEWHm2PJyck4d+4cbt68KbN9T4MGDdC6dWulvWn1s6ioKNSuXVu8KZW6FvPx48fimtROnTph/fr1uHjxolImxWXLls3SiOiTJ0+UqvYKgF+OfhcmP86mKuy6desmd80YZR8VT0lJgZOTE/bs2YPY2FiZcyVKlEDfvn0xadKkArF0hUmxguVkqouyF2UqKoYNG4Z169bB1dUVPXv2VNoRtKxo2LAhbt++jfDw8EJRSOtX2rZti7Vr16JHjx4wMDCAv78/SpYsiRYtWgD4Pp18z549kEqlaba9KUyUsZLt+PHjxeJ/hoaGGRYSS49EIhGnzCurYsWKwdnZGQsXLsTFixfTXe/eokULLF68WOlnugQGBmL8+PF4/fp1mhuwrq6uMDExwYYNG5R2NOZHWlpaMn9jihcvjpIlS8oUdwS+z4i4du1afoeXKxo3bgxXV1ds3749w/XTO3bswKtXr5Tqd1JmkpKS8OLFC5n36KtXr3D79m107NhRrI6vzBYvXoyqVasq9cyUjBSlUfGfTZ48GRcvXoQgCDA1NYWJiQmkUilCQ0Px+vVrbN++HcHBwZnuEJBfmBQrmIuLi6JDoDyWnJyMSpUqYd68eVixYgXMzc2hp6eXbnKs7F+2J02ahKlTp+K3335Dp06dUL16dZQqVapQFAVJz7Bhw+Dr6wsfHx+8fPkS6urqWLx4sVhp8ciRI/j8+TN69+6ttH/oC2sl20uXLkEikWDq1Kniz1lVWG5Ili5dGhs2bMCnT5/EonhJSUkwNDSEjY2NuJ2aMouIiMDw4cPx8eNHWFpaolOnTjAxMUFKSgpev36Ns2fPIjAwEGPGjIGbmxv09PQUHXKOmJiY4NmzZzLHKleujMePH8sci4uLE4uRKZuRI0fi1KlTWLt2Lfz9/fHbb7/B2NgYwPfigKdPn4aXlxe0tLSyVHSsoPPy8sLMmTNhamqKQ4cOicf9/f3x999/Y/Xq1Vi+fDlatmypwChz7uTJkzAxMVHav5WZKUqj4j9yc3PDhQsXULlyZaxZswY1a9aUOf/gwQPMmDEDHh4eOHv2rMK33WJSrGB2dnaKDiHffP78Gb6+vukeByBuOZUeZV5n82NxsejoaPj5+WXYVtm/bHfv3l2cDn/w4MFftlemoiDpUVdXx/bt2+Hn54ewsDDUq1cPZcuWFc+PHDkSVapUUdr3r4+Pj5g0Zia1kq0ySd0iI3V6ZVHZMiM9ZcqUQefOnRUdRp7YsWMHPn78iAEDBmDu3LlpfseOGTMGixYtwsGDB+Hi4oKJEycqKNLc0bRpU2zbtg2LFy/G5MmTUaJECdSqVQv79u3DpUuX0KZNG4SEhMDX1xcVKlRQdLhyMTExgZOTEyZOnAhPT094eXnJnBcEAcWLF8fatWvFHQCU1cOHDzF+/HikpKTA0NBQ5lyVKlXQunVreHp6YuLEiThy5EiG1biVQXJycpEeUS2Mjh07Bk1NTezYsUO8cfWjWrVqYfv27ejcuTOOHj2q8KSY1acpX2S1ymB6lH09xfHjx7PVPifrzBVt0KBB2WpflNZPKaOiUMmWvhdASUxMlDmWOgsgPDwc7u7umDx5smKCy6EOHTogLi4OHh4eUFNLfxwgKSkJrVu3RsmSJZW2ynaqiIgIdO3aFeHh4WKCHBwcjM6dO0NVVRXVq1dHUFAQ4uPjMWrUKEyZMkXRIcstIiIChw4dgq+vb5qt//r06ZOlYlwF3fjx4+Hh4YHVq1dneOPq7NmzmDp1Ktq3b5/uDh/KYubMmXB3d4erq2uRqk1SmH//2traolatWti5c2em7YYPH46AgADcuHEjnyJLH0eKFSw769h+pkxTbYvy3T9lTnKzqygnuVKpFPHx8ZkWjVO2dV9FoZJtUbZjxw44OzuLs3Uyo6xfyt69e4cWLVpkmBAD32d81KlTBz4+PvkYWd7Q19fH/v37sWrVKnHWSpUqVTBr1iysWLFC3BbO2toao0aNUmSoOaavr4+xY8cqOow89fjxY9SuXTvTmRydOnXCrl27cPv27XyMLPd16dIF9+/fR/fu3dGoUSNUr149w6VmADB48OB8jjB3FYXfvwkJCVm6Wa6jo4OYmJh8iChzTIoVLDvr2H6mTFNti+p6Cir8Tp8+jS1btuDly5eZJsTKOOOhKFSyBb6PFHp4eOD69esIDg7Gly9fIJFIoKenB0tLSzRt2hTNmjVTqt+5v3L69GmsXr36l+0MDAwUPqUtJzQ0NLL0ZSsmJqZAVD/NDRUrVkxzw3zw4MFo06YN7t27h1KlSsHe3r5QF30sLCIiIlC7du1ftqtQoUKateTKZvjw4eLyK29vb1y5ciXddqm7mShzUlxUfv8aGRnh/v37SE5OzvDGZHJyMu7fv4/y5cvnc3RpMSlWsKK8jq0oKOxftqOjo3N0vbKNnP7Mw8MD06ZNy1JbZVypUhQq2V66dAmOjo54//49gLT/Tnfv3sX+/ftRsWJFzJ8/H40aNVJEmLnuyJEjkEgkmDJlCvr37w83Nzc4OjrCy8sLxYoVg7e3NxwdHZGSkoLRo0crOly5mZub486dO3j79m2Ga2jfvn0LX19fpd42LdW4ceNQpUoV/Pnnn2nOGRkZFZpZWz4+Pjh48CBCQkKQkJCQYTuJRJKjwQdFK1euXJoiaekJDAxUuu2nfpaTbYuUTVH5/duyZUs4Oztj+fLlmDt3brptli9fjo8fP2LIkCH5HF1aTIoVrChNrZXXjRs3lK6ID1A0vmznpICUMo6c/mz37t0Avo/CDBs2DIaGhoVmtAko/JVsXV1dsWDBAkilUhgYGKBZs2YwMzODrq4uEhMTERkZiYCAANy8eROvXr3CyJEjsXz5cnTp0kXRoefYs2fPUKlSJXEKbZ06dZCSkoI7d+6gc+fOcHBwQJkyZTBs2DDs3LkTM2bMUHDE8unVqxf8/PwwcuRIrFmzJk0hoidPnmD69OlISkpCjx49FBRl7rlx4wa+fPmi6DDylLe3N8aMGZOlG43KnmQ1b94ce/fuxaZNmzKcibNr1y4EBwcrVfX/9CxfvlzRIeSbovL7d8SIETh+/Dj2798PPz8/dOjQQdzVIDQ0FOfOncPTp0+hr6+PESNGKDhaJsUF3ocPH/Dx40doaGigdOnSSn8n8Ed+fn7Yt29fplu9REREIDo6WumSp6LyZftXX0rU1dVRrlw5qKqq4tOnT+I0RgMDA2hra+dHiHkqICAA5ubmmD17tqJDyROFuZLty5cvsWjRIqSkpGDs2LH4448/oK6unm7b2NhY7Ny5E//++y/mzp2L+vXrF4ipXjkRExMjs/uBmZkZgO9JYur6xYYNG6JatWrw9vZW2i9lPXr0gLu7O7y9vdGjRw9UrlxZ5ktZSEgIBEFAs2bN0LNnTwVHm3OqqqrQ1dVVdBh5avPmzRAEAb///js6d+6MkiVLKn3ym5Fhw4bhxIkTcHJywrVr19CmTRuUL18eEokEHz58gKenJ3x9fVG8eHGlXyNelBSV37+lS5eGs7Mzxo4di4CAgDQ7jgiCACMjI2zcuBEGBgYKivL/MCkuoI4dO4Zt27bh9evXMsfNzMwwaNAg9OnTR0GR5Y4HDx5gyJAhSE5OzjCxSv0jZ2FhkZ+h5VhR+rL99OlTmZ+joqIwYMAAfPv2DXPmzEHLli1l1pHcuHEDCxcuhCAI2LdvX36Hm+skEgkqV66s6DDyzJAhQ/Dff//hwIEDCA0NxbZt29CvXz/s27cPkydPFivZJiYmimuMlcWhQ4eQlJSEqVOn/vLLpLa2NiZMmIASJUpg+fLlOHjwYJa2qirIdHR0kJSUJP6spaWF0qVLIzg4WKadqamp0hegcnJywoYNG7Bv3z68fPlSZvq/lpYWBgwYgEmTJhWKxKpXr17Yt28frl+/rpSzj7IiKCgINWrUwOLFixUdSp4rX748Nm/ejOnTp8PPzw/+/v4y5wVBgIGBAdatW1co9hUHgODg4HQHSwRBQEJCAj59+oSLFy9i//79CowyZ4rS719LS0ucP38e58+fx+3bt9NUiu/YsSM0NDQUHSYAJsUFkqOjI/bu3QtBEKClpQUjIyNIpVK8f/8eQUFBWLBgAZ49e4Z58+YpOlS57dixQ9wGo0ePHrhy5QpcXV3h5OQEqVQKb29vHDt2DFWrVoWrq6uiw82Wovxl+59//sHr169x8uRJVKpUKc35hg0bYvfu3ejUqRNWrVqFlStXKiDK3GNpaYnAwEBFh5Fn9PX1ceDAAaxcubLQVbK9ceMGDAwMsjVla8CAAdiyZQuuX7+u1J9T4PuXrcePH0MqlYpT/itXroxHjx7JtIuMjFT6ZFFdXR1//vknJkyYgIcPH8p8KbO2toampqaiQ8w1FhYWMDY2xvDhw2FmZpZpBV+JRIKZM2cqIMqc0dDQUKqbxzllY2ODM2fOwMvLCzdv3sTHjx+RnJyMMmXKwMbGBp07d4aWlpaiw8yxhIQETJgw4ZdJYGqhLWVWlH7/At8/sw4ODnBwcFB0KJliUlzAeHl5wcXFBQYGBpg/fz5at24t/jFLTk7GxYsXsWTJEhw8eBCtWrVCkyZNFByxfPz9/cW7mxoaGihRogQOHz4MFRUVtGrVCm3btoWFhQUcHR1x4MCBArEAP6uK8pdtd3d32NnZpZsQpypfvjzs7e0zrCypTP73v/9h3LhxOHLkCHr37q3ocPKEiYlJoaxk++7dO5nK2lmhrq6O6tWrp5kCpoxatWqFtWvXYuLEiZg2bRpMTU1Rv359+Pn54cCBA+jfvz/u3r2LO3fuiFP7lJ2GhgZsbGwUHUaemjlzpljBNygoCEFBQRm2VdakuFGjRvD19UVCQkKhuqGRGS0tLXTq1EmpKxH/yu7du3HlyhWoqamhWrVqiIyMxPv371G/fn1ERkbixYsXkEqlMDMzU+r9tYGi+ftXGTApLmD2798PNTU1bP9/7d15QE35/z/w52nTRiktkrWhrJlkz0jZVYpkG42dscwwPkx2ETODYez7XqTQeiWVNhRS2QuhorRIpaTt3t8fft2vtHO7p3N7Pf5y73kfnpHbeb3XI0cqbAgiIyODUaNGoU2bNrCzs8PZs2c5WxS/f/8eAwYMEE6Z+PKoF1NTUwDAzz//jKNHj4LH43GqKG7MD9v5+flVThX/Ep/PLzd1iKuaN2+OESNGYMOGDfD390fPnj3RrFmzKnt2uXyExNe4vpPtp0+foKqqWuf7VFVVv3vX9YZg6tSp8PT0RFBQEEpKSnDo0CFMmTIFx48fx6ZNm7B3717hhk1jx45lOS2prYULF0rEyFJ1/vjjD4wfPx7Lly/H+vXroa6uznYkIgJXr14FwzA4efIkjI2N4eXlBQcHB6xbtw4dO3ZESkoKFi1ahKdPn3JuD4uvSern78qVK7/5XoZhsGXLFhGmqTsqihuYBw8e4Mcff6xQEH+pa9euMDIywv3798WYTLTk5OQgLy8vfN28eXMoKSmVW0/BMAy6du2KO3fusBHxmzXmh+22bdvi1q1bePfuXZUPKq9evUJkZCS6dOki5nSiN2XKFOGozI0bN3Dz5s1K23H9XMXS0lKkpKTUuMu0gYGBGFN9n5KSkm/aKVxWVpaTO21/TUlJCefOncPBgwfRtGlTAICWlha2b9+O1atXIysrCwAwfPhwTn3fVvezsyaSsCP+4sWL2Y5Q71q1aoVly5Zh7dq1CAwMhKamJpo3b15pW4ZhcOnSJTEnFK1Xr17h1KlTePbsGT5+/FjtPixc/loTExPRvXt3GBsbA/i8LEcgECAmJgYdO3aEjo4Odu3ahZEjR+LEiROcXn4lqZ+/Hh4edWr/dQceFcWknPz8/Fr1eqqrqyM2Nrb+A9WTVq1aVdhQoG3bthU2birbhZpLGvPDtq2tLZycnDB9+nRs2LChwlTF0NBQODo6ori4GFOnTmUppeg0hnMVDx06hKNHj9bYYSMJBUVjUlpaChUVFfz555/l3h8xYgR++uknPHv2DM2bN+fc5j1cPA+c1E1oaCg2bNgA4PMzwtu3b/H27dtK23L98zk+Ph5Tpkypthguw/WvtbCwsNzsozZt2kBKSgpPnz4Vvte6dWsYGhoiKiqKjYgiJYmfv3UZKS4pKcHp06eFezw0hK+ViuIGRktLq1ZTaJ88ecLp45n69esHZ2dnHDt2DDNmzICUlBS6du2KCxcuIDo6GkZGRsjKysLdu3cb1YYaXDdlyhTcuHEDwcHB+Pnnn6GsrAwtLS0IBAKkpqaioKAAAoEAdnZ2nDt+qjKSfq7ihQsXsHPnTgCfZ3c0b95cos5hbsymTJkCXV1d/PvvvxWuKSgooEePHiyk+n51mVkUGxuLNWvWID09HQzDYPr06fUXTEz27t1bYxuGYSArKwtlZWW0a9cOxsbGDWb319rYt28fSktLYW5uDgsLC6ipqXG+IKzKvn37kJ+fj549e2LSpEnQ0tKS2M9gVVVVfPjwQfhaRkYG2traSEhIKNdOQ0OjwoZUkoTLn7+1Xer49OlTODg4CAviCRMmNIj9DagobmAGDhwId3d3HDlyBHPmzKm0zdGjR5GYmMjpg9pnzJiBS5cuYfv27YiNjcWePXtgZ2cHd3d3zJ8/HwMGDEBMTAzy8vIkonhqLKSkpLBv3z6cO3cOLi4uePHiRbkfcp06dcLMmTNhbW3NXkhSa66urmAYBitXrsTkyZNrtV6cS+Lj42tVRHx9jySIj4+HkpIS2zFErmwqYnVKSkqwZ88eHDt2DCUlJdDV1cXff/8tnLbJZXv37q1QIJaNMFZVOKqrq2Pbtm3o379/vecThWfPnkFfXx/79u1jO0q9u337NjQ1NXHq1CmJ31Ssc+fOiIqKQlZWFtTU1AB8Pu0gNjYWRUVFwo6b5ORkKCoqshm1zoKCggB83iROQUFB+Lq2zM3N6yMWK44ePYrdu3ejqKgIGhoacHJywuDBg9mOBYCK4gZnzpw58PHxwY4dOxATEwMLCwvo6uoCAF6/fg1fX18EBwdDQUGhyqKZC3R0dHD8+HE4OjpCRUUFwOf1IzNmzMCJEydw5coVYbuFCxeyGfWbNOaHbSkpKUydOhVTp05FRkYG0tLSAADa2toN4nD2+pCRkYGzZ88iMjISqampMDU1xYYNG7Bz507o6+tzdsfQ58+fo3v37pxa01QXT58+LTc1rzYk4TgQAJx7qBSVp0+fYsWKFYiPjxfOWnFwcJCYv49//vkH586dQ2xsLNq2bYvhw4dDV1dXOFsnKCgIz58/R9u2bTFo0CAkJSUhPDwcCxcuhKenJ9q0acP2l1AjeXl5TuQUhYKCAvTp00fiC2IAsLKyQnh4OCZOnIilS5di9OjRMDU1xfXr17F27VrMnTsXAQEBePz4MYyMjNiOWydlG+BdvnwZ7du3r/OGeFzfhBX43Jnh4OCA6OhoCAQCjBo1Chs2bBDWAA0BFcUNTOvWrbF371789ttvuHbtGoKDg8tdFwgEUFZWxo4dOzj/Q8HQ0BCXLl3Cp0+fhO/9+eefGDp0KKKjo9G8eXOMGjWKk6MZjflh+0saGhqcnuZfG6GhoVi2bBny8/OF/4YfP34EAFy7dg2HDx9GdHQ01qxZw3LSupOTk4OmpibbMeqFjY0N2xFYNWPGDOzcuRPu7u4YP348547UqiuBQICjR49iz549whGKzZs346effmI7mkgpKioiNjYWdnZ2WLduHWRkyj/mLVmyBP/88w9Onz6NwYMHY9CgQbh69Sp+++03nDp1CmvXrmUpee0NGDAAt27dKjd6KKnatGmD9PR0tmOIhYWFBa5duwY/Pz9cuXIFo0ePxvjx43Ho0CF4e3vD29sbwOcZDzNmzGA5bd307t0bAITnSZe9bixcXV2xdetWfPz4ESoqKli/fn2DHCxgBLQrRYOUlZWF8+fP4/bt28I59y1atEDfvn0xefJk4dQS0vB8z5b0APDXX3+JKAl77t+/j2fPntW4WzHXRyATEhIwbtw4lJaWYsKECTA1NcW8efNgZWWFrVu34tSpU9izZw/y8/Oxb98+mJmZsR25TmbNmoWXL18iMDBQ4oumxubAgQPw9vbGq1evoKSkhE6dOkFVVbXSf2eGYSqcVc0lycnJWLFiBWJjYyEQCDBmzBisW7euQY1QiMqkSZOQlpaGgICACgVxGT6fj+HDh0NLSwsuLi4AAEtLSxQVFcHf31+ccb9JcnIyxo8fj169emHdunUSve/I8ePHsX37dpw7dw6GhoZsxxGL4OBgFBYWYuTIkQCAFy9eYOPGjYiJiUHz5s0xe/Zs/PzzzyynJLWRnp6OVatW4caNGxAIBPjpp5+wefPmBjtYQiPFDVBubi727t2L7OxsnDhxQvh+QEAAli1bhjdv3mDFihXfdOxPQ1RaWopPnz5Vu7OisrKyGBN9H0koar9VQUEBFi1aVOXRRGW4fkRRmQMHDqCoqAi7du3C8OHDK1z/5Zdf0K1bN/z8889wcXHhXFG8YMECTJs2TTh7RZIlJCRAT0+P7Rhis2vXLuGv8/LyEB0dXWVbLs9gOXv2LLZt24aCggKoqqpi/fr1GDVqFNux6k18fDwGDx5cZUEMfF7i0q1bN4SGhgrfa9++PcLDw8UR8bsdO3YM+vr6CAkJQUhICFq0aAFVVdVKv2auH1M0ffp0xMTEYO7cuZg5cyZ69uxZbWcOl47Fq8qQIUPKve7QoQNOnjzJTph6snDhQnTo0AHLli1jO0q98fHxgZOTE3JycqCoqAgHBwfY2dmxHataVBQ3MLm5uZg8eTISEhIqbE+en5+P0tJSeHh44MGDBzh//jyn10H5+vriwIEDePnyZbUFMR31wh2HDx/GjRs3ICMjg169ekFTU7PahzOui4yMROfOnSstiMv06tULhoaGeP78uRiTiUZKSgqGDh2KAwcOgMfj4ccff0SzZs0qLZIYhmkQu0d+qzFjxsDIyAgTJkzAqFGjyp2jLokkvfMuLS0Nq1atws2bNyEQCDB48GA4OTk12BEKUVFUVBTu41Cdt2/flvtsLikp4cxUZFdX13KvMzIykJGRUWlbLnfoABDuQlxSUoL//vuv2rZce1by9PT8rvu5vGFnREQE3r9/z3aMepGdnY1169YhICAAAoEAxsbG+OuvvxrEkUs1kdynVY46duwYEhISYGZmVmFtj7W1NUxNTYXfbIcPH8aSJUvYCfqdgoKC8L///a9Wbbk2w7/sPNey0e2aznf9GpdGxb92+fJlyMrK4uzZs+jevTvbcepdTk5OrTb80NDQwKNHj8SQSLSWL18OhmEgEAiQmJiIxMTEKttyvSjW09NDdHQ0YmJisHnzZowZMwa2trYS8328d+9eGBgYYOjQoQAke021l5cXNm/ejNzcXCgpKcHBwYHTpzXURffu3REaGoqwsLAq10tfv34dsbGxGDBgAIDPP2MfP34MbW1tcUb9ZqdPn2Y7gthI6p4OAODg4PBdnRZcLoqlpaXRrFkztmOI3LVr17Bu3Tq8e/cOcnJyWLp0KaeOuqOiuIEJCgqCtrY2du3aVenxJ6qqqti+fTuGDh0KPz8/zhbFZdPC7e3tMWPGDGhqakrM2XvGxsaQkpICj8dD+/bt67ShAtd6er+WkpKCvn37SkwhUZPmzZvj1atXNbZ78eIFJ/cBqOsOmVzG4/Fw7949uLu7w8/PD+fPn4ebmxv09fVha2sLKysrTj/E7N27F1ZWVsKiWFItXrwYgYGBEAgE0NHRwerVq9GqVSvExcXV6n6uTz+dPXs2wsLCsGjRIsyYMQPDhw+Hjo4O+Hw+3rx5g6CgIJw8eRIMw2D27NkoKSnB6tWrkZaWxpmOkj59+rAdQWyuXbvGdoR6JycnB2NjY4mfnfMlW1tbODs74+bNm8LOKUmwYMEC4TODsrIyvLy84OXlVat7G8JSByqKG5g3b95g0KBB1Z4HKicnhx49epRbD8Q1jx8/RqdOnbBq1Sq2o9SLLzeXqstIN9dGxb+moqLSqDZk6tu3L3x9fREUFFTlOYL+/v5ISEiAhYWFmNN9v8WLF7MdQawMDQ1haGiI1atXw8/PDxcuXEB0dDScnJywbds2DBs2DLa2tujXrx/bUUkVAgICwDAMGIZBamoqFi1aVOt7ud4pCXzulN24cSM2bdqEw4cP4/Dhw+WuCwQCyMjIYO3atRgwYACSk5Ph5eUFdXV1TJ06laXUpDEaMmQIrl+/jsLCQsTGxmLYsGGwtLTEgAEDJL4zVl9fH7q6upg1axb09PTQuXPnajc65NIsrLLn2Hfv3uHdu3e1vq8h/JtTUdzAKCkpISsrq8Z2eXl5wq3duYhhGLRr147tGPXi6xGJ2o5QSAITExMEBwfjw4cPaNq0Kdtx6t3cuXNx5coVLF26FLNmzUL//v0BAMXFxUhKSkJQUBB2794NGRkZzh0h0ZgpKChg3LhxGDduHJKSkuDn54erV6+Cx+OBx+OhdevWmDBhAiZMmCAxGx5KCh0dHbYjsM7W1hZ9+/YVjkS9efMGJSUlaNmyJQYMGIBp06ahQ4cOwvYLFiyAnZ0dp86Rf/r0Kdzd3fHq1SsUFhZW2Y5hGJw6dUqMyUhtHThwADk5Obhy5Qp8fX2Fo4rq6uqwsLCApaUlunbtynbMelE2dVwgEOD58+fV7jnCpaKY60sb6EimBmb+/PkIDw/H+fPn0a1bt0rbPHv2DDY2Nujdu3e53am5ZOrUqcjKyoKfnx/bURqUtLQ0aGlpsR3jm719+xY2NjYwNDSEo6Mjp7+W2vLz84ODgwOKiooqvS4tLY3169dzdk1jaWmp8MG6uLi43GwGPp+PoqIiZGRkICgoSCKn+qWnp+Pq1au4cuUKoqOjy80CUVBQwK+//oq5c+eymLB2DAwMhEeFEcJlsbGxmDZtGkpKSmqcXcUwDJ48eSKmZKLXuXPnWrfl+kyHt2/fwsfHB76+voiPjxcOnlhZWcHCwoITGzXV1p49e+o0MlqXGS/k21FR3MCEhYVh7ty5UFVVxbJlyzB06FA0b94cwOdNfYKDg7F9+3a8e/cOe/bs4ez6sKCgICxcuBAbN25s8Fu0f69z585h8uTJNbZzd3fH1q1bcefOHTGkqh9r165FYmKi8Gto2bJltbsVs71+RFQSExNx8uRJREZGIjU1FXw+HxoaGujTpw9++eUXzq5TzM3Nhb29PeLj46ttV3bEFpcfPr9UVFSEwMBAXLp0CREREcJCuG/fvrCzs0OvXr3A4/Fw9OhRvH//HosXL8aCBQtYTl09Koobn6KiIty/fx/p6emQk5NDixYt0LVr12qXZ3GBvb09bt++DRMTE4waNarG86a5+pwE1H6Nu66uLmRlZSVmoCEhIQHe3t7g8Xh4/fo1GIaBoaEhrKysMGrUKOFzMeGeFy9e4N27d3Xab0dcqChugLZt24Zjx44JC4kmTZqAYRh8+vQJwOcH0GnTpmH16tVsxvwu0dHROHXqFAICAtC/f3/07NmzyuIJAKfPs+3SpQscHR2rHClMTU3FmjVrhGf7crmwqEvxJ0lFlKTavn07jh49CmVlZfTr1w+vX7/G06dPYWlpidzcXERHRyMnJwcdO3bEtm3bOFv8l7l//z4uXboEPz8/5ObmQiAQoEWLFrCxsYGdnV2FkYonT57AxsYGGhoaDf6MVwMDg29es8X1Eaivubm5ISYmRmKPpeLz+di7dy9OnTqFjx8/lrvWtGlTTJo0Cb///jtnN7fs27cv1NTUcPny5QaxDrE+ffjwodL3S0tLkZubi7t372L37t1o3bo1jh07xvkOj8pER0fD19cX/v7+yMrKgrS0NExMTHDw4EG2o4lUaWkpSkpK0KRJE7aj1Kvly5fD19e3QT7/0ZriBmj58uUwNjaGs7Mz7ty5IyyGZWRkYGhoCHt7e4wYMYLllN9nypQpwvUUN27cEBaEXysbgeJyUaygoID169dDRkamwu6e58+fx7Zt25CXlwdlZWX88ccfLKUUDa6vJ/leBQUFyMjIgLS0NDQ1NTn/gBISEgIZGRm4u7ujffv2wvXT06ZNQ7du3ZCfn4/ffvsNN2/erPLhjSvGjBmDFy9eQCAQQEpKCgMHDsTEiRMxZMiQKs/a7ty5M5o0aVLnY9fY8q194JLWd37nzh34+vpKbFG8ZMkS4Rmh7du3R+vWrVFaWork5GQkJSXhyJEjePHiBfbu3ct21G8iEAjwww8/SHxBDKDavTlUVVXRpk0b9OjRA2PHjsWRI0ca/IyVb2FkZISePXti8ODB2Lp1KxISEji90eyX4uLicPz4cURGRiIjIwNWVlb4559/sGrVKrRv3x4zZ87kbOcVF1FR3EANGTIEQ4YMAfD5IOySkhKoqqpW+XDGNdbW1o3iBxoAHD9+HLNmzcKaNWsgLS0NKysrpKSkYPXq1YiMjIRAIICZmRnWr1/P+TW4jemojC/xeDw4Ozvj/v37wqm20tLS6N+/P2bOnCncgItr3rx5g549e6J9+/YAgK5du0IgECA2NhbdunWDkpIStm3bBlNTU5w+fbpBToeqrYSEBGhpaWHcuHGYMGFCrTZsKiwsxNixY6vc/6GhGTZsGGc2bCHfxtPTE1evXkW7du3w77//Vtio6P79+1ixYgWCgoJw+fJljB49mqWk387IyAhxcXHCTvPGTk9PD8bGxvDy8pK4ojgyMhKXL1/G1atXkZOTA4FAABUVFc4PDAGfl8w5OjqipKRE+F5ZB2RMTAw8PDxw79497N69u1Gd6sEmyaiwJJwk7m76999/sx1BbAwNDXHy5EnMmjULK1euRExMDLy9vZGfn48WLVpgzZo1GDlyJNsxxS4pKQlt2rRhO8Z3W7FiBXx8fCAQCCAtLQ11dXUAQFZWFsLDw3H9+nUsXLiQkxtllJSUQENDQ/i6VatWkJGRKbdTppqaGn788Ufcu3ePjYgic+DAAQwePLhODx9NmjTBxo0b6zGVaCkoKKBVq1ZsxyD16OLFi2jSpAmOHj0KXV3dCtd79OiBI0eOYMyYMbhw4QIni+IlS5Zg4sSJ2Lx5M1asWAE5OTm2I7FOSUkJqampbMcQiZiYGPB4PPj7+yMzMxMCgQAKCgoYPXo0LC0tYWJiwvkBotjYWKxfvx6KioqYP38+TE1Nyx3buHTpUjg5OSEoKAg+Pj4YO3Ysi2kbD25/VxHCEd26dcOpU6cwffp0uLq6gmEYTJgwAStWrJC4o4uSk5Ph5uZW5W7FhYWFyMzMxLNnzzi/TtHd3R3e3t7Q1NSEg4MDzM3NheuBCgoK4O/vj61bt2Lfvn3o2bMnTExMWE5cN2pqauXOGZSSkoKOjk6F4yNUVVXx/v17cccTqatXryIzM7PGXcIPHTqEGzduNPqlAqRhiouLg7GxcaUFcZnWrVujd+/enP38NTAwwIYNG7By5Ur4+vpCT0+vyo2XGIbBnj17xJxQvDIzM3H79m1Obz71+PFj8Hg8+Pn5ITU1VXiedlmxaG5uDnl5ebZjisyRI0cAAAcPHoSxsXGF68OHD4eenh6srKzg7u5ORbGYUFFMWPXu3Ts8e/YMBQUF5Y46+Zq5ubkYU9UPAwMDnDlzBtOnT0dOTg769+8vcQVxQkIC7Ozs8PHjR2ExXLZ2vOzXAIRToLjO1dUVTZo0walTp4RTjMsoKCjA2toanTt3xvjx43HixAnOFcXdu3dHSEgIXr58Kfz69PT0EBERIVwHD3w+Jo7r/54eHh4oLS2tsSiOiYlBbGyseEIRUkeFhYVQUlKqsZ2SkhLy8/PFkEj07t27B0dHRwCfl5fdvXu3yrZcn15dXedbaWkp0tPTwePxkJeXx7lR/4SEBPB4PFy+fBmJiYnC/Rx69+4NS0tLDB8+nPM/V6oSHR2Nnj17VloQl9HT00OvXr3w4sULMSarf82bN0fLli3ZjlEpKooJK0pLS+Ho6IgLFy7UahOXhrhLXVVq2rylS5cuCA8Px4oVK3D79u1yOw1y6ZD2yhw5cgT5+fno0aMHLCwscPfuXQQEBGDdunUoLCxEWFgYbt68iU6dOsHT05PtuN/txYsX6NevX4WC+Ev6+vro27cvHj58KMZkojFhwgQEBgZi0qRJWLhwIezt7TF8+HAEBwfj999/h729PYKCgvDixQsMGDCA7bh1smXLlgqbg8XGxmLlypVV3pObm4vQ0NByU8oJ9wwfPhxt27ZlO0a90NHRwb1791BSUlLlFNOSkhLcu3evwT6Y1uTff//Fp0+f0K1bN4wcORJqamqcL36rsmXLlhq/NoFAAF1dXSxevFhMqURjzJgxwk7zbt26wcLCAmPGjGkUn695eXlo0aJFje2aNm2K3NxcMSQSn1WrVmHVqlUV3s/JyWG9E4SKYsKKU6dOwc3NDcDn8/U0NTUlZoe9U6dOlRsdrUpJSQlcXV3Lvcf1ovj27dto1qwZjh8/DmVlZXTo0AFXr15F27ZtMWDAAEyfPh3bt2/HsWPH4OnpiXHjxrEd+bvIy8vXqlOntu0amsGDB2P27Nk4duwYYmJiYG9vDwsLCxw5ckS4a3xZ7/6vv/7Kdtw60dHRKbe3AcMwSEpKQlJSUo33Tpw4sT6j1YtFixZBX1+f7RgNwrBhwzBs2DC2Y9SLIUOG4OTJk/j777+xZs2aStv8/fffSE9Pxy+//CLmdKLx6NEjtG3bFq6urpxfW1qT6jYlZRgGioqKMDAwwOjRo6GoqCjmdKKhoqKC0tJSeHl5wcvLq1b3MAyDS5cu1XOy+qOpqYn4+Pga28XHx3O6k8Dc3BzDhw/Hn3/+WW275cuXIyIiAtevXxdTsspJ9qcJabA8PDwgJSWF3bt3Y+jQoWzHESkubqgkKpmZmejdu7dwWq2+vj4EAgEePnwoHEn8/fffceHCBXh4eHC+KP7pp59w5coVvHr1Cu3atau0TVZWFm7duoWffvpJvOFE5H//+x8sLS2RnZ0NAJCVlYWzszP+/fdfxMTEoHnz5pg5cybndp6eNm0aZGRkUFpaCoFAgL///hvdunWDpaVlpe0ZhoG8vDw6dOhQ7ZS3hqqxfC7FxcUB+Dz1UFZWVvi6trh+1vbs2bPh4eEBFxcXREdHY+TIkcLztZOTk+Hn54e4uDioqalh9uzZLKf9NtLS0tDX15f4ghiQ/E1JBQIBcnJykJOTU6f7uD4zwMTEBG5ubnB1dcWkSZMqbXP27Fm8fv0adnZ2Yk4nOm/evCm3L0lVkpKSGsSIuOR/opAGKTExEb169ZK4ghhoPA+flZGWlhYWxACgoaEBBQWFcmtiZGVlYWhoKBHrMv/880/ExsZi+vTpcHR0xODBg8tdj4uLw6pVqyAvL49ly5axlPL7fT3CqKamhs2bN7OURjSkpaXx888/C1+fPn0axsbGnB09+1b379+v1b4OXDkr3traGlJSUuDxeGjfvn2djv9jGIazm0+VUVdXx8mTJ7FgwQI8fvy4wtIjgUAAHR0d7Nmzp1bTNxsiIyMjPH78mI5k4rjGvFnhvHnzwOPx4OjoiOjoaOGxjTk5OYiIiEBQUBBcXV2hoKCAWbNmsZy29mbNmoVXr16Vey8wMLDafYHy8vKQm5tb7TI0cWEEXJzTRzivf//+6NmzJw4cOMB2FCJCI0aMgJKSUrlpTZaWlpCRkYGHh4fwvYULFyIsLAwPHjxgI6bITJ06Fbm5uXj27BkYhkHTpk3Rpk0bSEtL4+3bt0hPTwcAyMnJlVs7Dnx+AL916xYbsQlBQUEBFi1ahJs3b1bbrqzw4Mq+DmZmZgA+L2Np3bq18HVtXbt2rT5iiV1RURGuXLmC27dvIz09HQKBAJqamujTpw9GjRrF6WOMnjx5Ajs7O4wbNw6rVq2q8NkqiV68eFHpiQ4CgQCFhYXIyMhAQEAAXFxcWExJ6uLOnTv47bff8P79+wqdOwKBAEpKStixY0eFzvaGLDQ0FPPmzRO+rs1SQuDzxqTbt29nfVNdGikmrOjduzeio6NRWFjYKH6gAZI3IlOZ3r174+LFi/Dz88OoUaMAfJ6OyOPxkJCQAD09PXz69AmxsbGcXidT5stdTwUCAXJzcyvdUKuwsBCFhYXijPZN9u7d+133c2mWRGOfZnv48GHcuHEDMjIy6NWrFzQ1NSViOurXRa2kFLl1JScnBysrK1hZWbEdReSuX7+Ovn37ws3NDd7e3tDX14eqqmql379cP5KpsLAQixcvRnh4eLXtaNSce3r37g1/f3+4ubkhMjISqamp4PP50NDQQJ8+fTBx4kTOPScNHjwY3t7e4PP5EAgEsLGxgampKZYsWVLlPfLy8mjZsmWDqAVopJiw4vnz57C1tYWFhQXWrVvH6V7rmkjqiExlnj9/DhsbG5SUlGD8+PFwcnJCREQEZsyYAR0dHYwaNQo3btxAfHw8LCwssG3bNrYjf5c3b9581/2tWrUSURLRMDAwKPdg9fVRWlXh4veugYFBuWm2X3/t1ZGEabYjRoxASkoKzp49i+7du7Mdh5BaK/u/WpvHV659Ln3t4MGD+O+//yAjI4OOHTsiJycHqampMDY2Rk5ODhISElBaWgo9PT0sXbpUIpekEe5auXIljIyMajzqsKHgfrcw4aTAwED069cPFy9exJUrV9C5c2c0a9as0odSrvf0SuqITGV++OEH/Pfff1i/fj1KSkoAfJ4qP2LECPj7++P48eMQCARo1qwZfvvtN5bTfr+6FLVc6smXk5PDTz/9xNkjW2pDR0cHAIT/F8teNxYpKSno27dvoyqIS0tL8enTp2qLqS/3ROCqqKgoHD58GM+ePSt3ZvzXuLqEo6ZjDyXJ1atXwTAMTp48CWNjY3h5ecHBwQHr1q1Dx44dkZKSgkWLFuHp06cNrpOVVM3e3h4DBw4sN9W4Mn/99RdCQkLg7+8vpmSiVdn/1dLSUpSUlDSIkeGvSeaTOWnw/vvvP2FPb15eHu7cuVNlW64UElW5fPkyZGVlG82IjLm5OQYPHlxux8H//vsP3t7ewt2K7ezsJKLgOnr0aK12cE1KSsKKFSsqHMHV0Pz888+4cuUKMjMzERQUhF69esHS0hIjR45Es2bN2I4nUo19mq2KigqkpKTYjiEWvr6+OHDgAF6+fFltQSwJMwCioqIwffp04a7qksjGxobtCGKTmJiI7t27C3e87969OwQCAWJiYtCxY0fo6Ohg165dGDlyJE6cOIGtW7eynJjUxu3bt6GtrV1ju/j4eKSkpIghUf2Ki4vD8ePHERkZiYyMDFhZWeGff/7BqlWr0L59e8ycObNBHMtKRTFhRWPq6W2MIzIyMjLQ0tISvmYYBmPHjsXYsWNZTCV627dvR0REBP75558qd3J1c3PD33//jYKCAjGnq7s1a9Zg1apViIiIgLe3NwIDAxEVFQUnJycMHjwYlpaWMDU1lcjlDm/fvq3VQ4qkMDExQXBwMD58+ICmTZuyHafeBAUF4X//+1+t2kpCEXno0CGUlJRg+PDhsLe3h6amZoN42KxPZZsaysnJQV1dnXPrMKtTWFhYbhZLmzZtICUlhadPnwrfa926NQwNDREVFcVGRFILy5YtQ0ZGRrn3bt68We0eMrm5uYiPj+f8LCY3Nzds3LhROHsQ+L/P2piYGHh4eODevXvYvXs36x21VBQTVjSmnt7GNCJTlbt37yI5ORnW1tZsRxEpXV1d3LhxA2PHjsWWLVvK7RKZlZWFVatWITQ0FAKBAGPGjGExae1JSUlh4MCBGDhwIAoLCxEUFARfX1+EhIQgMDAQysrKGDFiBCwtLdG3b1+244qMubk5+vXrBxsbGwwbNqxBTu0SpSVLliA0NBTLly+Ho6NjuU4sSXLixAkAn6crzpgxQ+KLxNjYWLRp0wa7du3i/Cyrmly8eBGHDx9GUlJSuff19PQwbdo0TJw4kaVkoqOqqooPHz4IX8vIyEBbWxsJCQnl2mloaFS6ySNpGAYNGgQHBwfha4ZhkJmZiczMzGrvYximxinWDVlsbCw2bNgARUVFzJ8/H6amprCwsBBeX7p0KZycnBAUFAQfHx/WB06oKCYNQmFhIdLT0yErKwttbW3w+XyJKSQby4hMdVxdXeHr6ytxRbGXlxecnJzg4eGB+fPnw97eHv/73/8QGhqKdevWISsrC1paWli/fn2dj4ZpCJo0aYLRo0dj9OjRyM3NxZUrV+Dj44NLly7h4sWL0NTUhIWFBSwtLTm/G7OysjJu3LiBmzdvQklJCaNGjYK1tTV69erFdrR6sW/fPnTs2BGhoaEwNTVFy5Ytq93X4ctj1rjk8ePH6NSpE1atWsV2FLEoKSmp06ZxXLVlyxacOXMGAoEACgoK0NHRQWlpKVJTU/H8+XNs2LAB8fHxWLduHdtRv0vnzp0RFRWFrKwsqKmpAQA6dOiA2NhYFBUVCWftJCcnQ1FRkc2opBrW1tZQUVFBaWkpgM8nNfTp06fKkWKGYSAvL4927dpxeq34kSNHAHzeMK5sCcCXhg8fDj09PVhZWcHd3Z2KYtK4hYSE4MiRI7h37x5KS0uF6wwWLlwILS0trFixgvMf9JI8IlPbtS5lU4dTU1PLTVHk+rQgJSUl/PXXXxgyZAjWrVuH06dPw8/PTzhNys7ODitWrJCIzXuaNWsGOzs72NnZIS0tDb6+vrh8+TKOHTuG48eP44cffoCPjw/bMb/ZjRs3EBISAm9vb4SGhsLd3R0XLlxA69atYWNjg7Fjx3L++/VL7u7u5V6npKRU+f+ZywUWwzBo164d2zHERk9PD8nJyWzHqFfBwcE4ffo0WrRogfXr18Pc3FzYiV5SUoKAgAA4OTnh3LlzMDMzg4mJCcuJv52VlRXCw8MxceJELF26FKNHj4apqSmuX7+OtWvXYu7cuQgICMDjx49hZGTEdlxSjSFDhgh/3bt3bwwcOFDidwuPjo5Gz549Ky2Iy+jp6aFXr1548eKFGJNVjopiwprdu3fjwIEDEAgEkJKSgkAgEBZMz58/R0hICOLi4nD69GlOr2GU5BEZMzOzOj0wfzlaKgmb2pQZPnw4VFVVMXPmTKSnp4NhGIwZMwYbN25kO1q90NLSwrhx46CsrIzTp08jISEBz58/ZzvWd5GRkcHQoUMxdOhQ5OXlwc/PD97e3oiKisKuXbuwZ88e9O7dG+PGjcOIESMgLy/PduTvcvr0abYjiIWBgQGePXvGdgyxmTx5MlavXo3g4OByD+GSxMXFBTIyMjhy5Ag6d+5c7pqMjAxGjRqFNm3awM7ODmfPnuV0UWxhYYFr167Bz88PV65cwejRozF+/HgcOnQI3t7e8Pb2BvD55+mMGTNYTktq68yZM2xHEIu8vLwq91v5UtOmTZGbmyuGRNWjopiwIjQ0FPv374eWlhZWrlyJQYMGlZumuGPHDqxatQr37t2Du7s7pk6dymLa7yPJIzK6urp4/fo1AEBRUbHKtXoFBQUoLS2ViBHTr5WWluLgwYPCDW46duyIpKQk8Hg8pKamYvPmzRIzUvXhwwdcvXoVly9fxq1bt4Q73Hbs2BGWlpZsxxMZZWVlTJgwARMmTMDbt2/h7e0NPz8/3Lp1C7dv38amTZs4v6lNnz592I4gFjNnzsTChQvh5uYGOzs7tuPUOxMTE4wYMQK///47rK2t0bNnzyo7YYHPa+m55sGDB/jxxx8rFMRf6tq1K4yMjHD//n0xJhM9hmGwc+dOWFlZobCwEACgoKCA06dPw9HRUXiiw+zZszFs2DCW05K6evHiBd68eYPi4uJys+gEAgEKCwuRkZGBgIAAuLi4sJjy22lqaiI+Pr7GdvHx8Q1igzwqigkrTp8+DVlZWRw/fhx6enoVrnfv3h3Hjh3DsGHD4OXlxemiuC4jMl/vTtjQeXp6YsOGDfD19UWHDh2wffv2SgvA5cuXw9fXt9qjt7jo4cOHWL16NZ4+fQppaWksXLgQCxYswIsXL7B8+XLcvXsX1tbWWLRoEWbNmsW5Tg/gc4dGUFAQeDwebty4IfzhraurizFjxsDCwgIdO3ZkO2a90dbWhpmZGUpKSpCdnY3U1FTk5+ezHUvkEhMTkZKSAhUVFXTp0gW5ubkScQxX8+bNMWLECGzYsAH+/v41FonV7QbLBYMHDxYed+ju7l6hU/ZrT548EVMy0cnPz4e6unqN7dTV1REbG1v/gcTg61H/9u3b4+TJk+yEId+tsLAQixcvRnh4eLXtBAIBJ58bypiYmMDNzQ2urq6YNGlSpW3Onj2L169fN4hOSyqKCSsePHiAXr16VVoQl9HU1ISxsTEePXokxmSiV5sRmaioKLi4uCAwMJAzuxQDn0fUtm/fjkGDBmHTpk2wsbHBqlWrMGHCBLajicXEiRNRWlqKtm3bYtu2bejRowcAoGPHjrhw4QJ2796NY8eO4d9//4W/v3+ND6gNRVFREUJDQ8Hj8RAaGopPnz5BIBCgRYsWGDlyJCwsLNCzZ0+2Y9arlJQU+Pr6wsfHRzg1XF5eHpaWlhg3bhzL6UTHxcUFR48exdu3bwEAlpaW2Lp1KxYtWoQmTZpgy5YtDaIH/1tNmTJFWCSWbaRWmbKHT64Xxb1792Y7Qr3T0tKqVTH/5MkTTn/vEsl14sQJhIWFQUZGBh07dkROTg5SU1NhbGyMnJwcJCQkoLS0FHp6eli6dCnbcb/ZvHnzwOPx4OjoiOjoaPTv3x8AkJOTg4iICAQFBcHV1RUKCgqYNWsWy2mpKCYs+fTpE5SUlGpsJysry4nzXb9FQUEBvLy8cPbsWTx79ozTPYJjx46FkZER/ve//2HdunUICQmBk5MTmjdvzna0elVaWoopU6ZgxYoVFdaYysjI4I8//oCpqSlWrFjBieMyQkNDcfnyZQQFBSE/Px8CgQBNmzaFtbU1LC0t0a9fP4nZFb4y2dnZ8PPzg4+PD2JjY4X7HBgZGcHGxgajRo2SqCUADg4O8PLygkAggJqaGrKysoTX3r59i6SkJPz888+4cOECZ3fOt7a25uzn6rdoDGsVBw4cCHd3dxw5cgRz5syptM3Ro0eRmJjIuQ7av/7665vvZRim3LE/pOG6evUqGIbByZMnYWxsDC8vLzg4OGDdunXo2LEjUlJSsGjRIjx9+pTTu0/r6OjgwIED+O233+Dt7Q0fHx8wDIOwsDCEhYVBIBBASUkJO3bsQNu2bdmOS0UxYYeOjg4eP35c7dFLJSUlePz4MVq2bCnmdPXr5cuXOHv2LDw9PZGXlycshnv37g1bW1u2432z1q1b4+zZs9izZw8OHz4MKysr/PXXX5ze5KQmR44cwaBBg6ptY2RkBC8vL/z9999iSvXt5s2bB4Zh0KRJEwwfPhyWlpYYPHgwZGVl2Y5W7+bPn4/r168L10m3bNkSY8eOxbhx49CmTRu244mcl5cXPD090bFjR2zZsgXdu3cvd6zW6dOnsWLFCty5cwcuLi6YP38+i2m/HRf+35G6mTNnDnx8fLBjxw7ExMTAwsICurq6AIDXr1/D19cXwcHBUFBQqLJobqhOnTolnNlQV1QUc0diYiK6d+8u3JW5e/fuEAgEiImJQceOHaGjo4Ndu3Zh5MiROHHiBLZu3cpy4m/Xu3dv+Pv7w83NDZGRkUhNTQWfz4eGhgb69OmDiRMnNpgZHVQUE1aYmZnhxIkT2L17N5YsWVJpm3379iEjIwO//PKLeMPVA4FAgGvXrsHFxQURERHC97S0tGBtbQ1bW1u0bt2a5ZTfT1paGkuWLIGJiQmWL1+OOXPmYOrUqcINQiRNTQVxGSUlJWzatKme04hWRESE8Hu1NhiGwa1bt+oxUf0KCQmBgoICRo0ahXHjxqFfv34SPcLo5uYGBQUFHD16tNJj4rS1tbF//34MGTIE/v7+nC2KG6v379/j/PnziIyMRHp6OuTk5KCuro5+/frB2tq6wTyEfovWrVtj7969+O2333Dt2jUEBweXuy4QCKCsrIwdO3ZwrkNr0aJFbEcgYlBYWFjuiL82bdpASkoKT58+Fb7XunVrGBoacn5TR+DzkY6zZ8/G7Nmz2Y5SLSqKCStmz54NX19fHDp0CFFRUejXrx+Az+fYuru7IygoCKGhoVBVVW0Q6wy+1fv37+Hu7g5XV1fhGb2ysrKQk5PDx48fERoaynbEemFsbAxvb2+sWbMGzs7OElNcxMXFQVVVFdra2nW6z83NDbGxsdiyZUs9JRMdgUCAT58+4dOnT2xHEatNmzZJ3PTo6sTFxcHY2Ljac9OVlZVhZGSEmJgYMSarH0+ePMHLly9RVFRUbTtra2vxBKpHt2/fxuLFi5Gbm1thxPHmzZs4duwYdu7cKVzfx0UDBw5EQEAAzp8/j9u3byM9PV2470Hfvn0xefJkqKmpsR2zzqgobhxUVVXx4cMH4WsZGRloa2sjISGhXDsNDQ1OLL2SFFQUE1aoqanh+PHjWLx4MaKionD37l0AnzecioqKEk5f3L17Nyd7tO/fvw8XFxdcuXIFRUVFEAgE6NChAyZMmABra2v88ccfiIyMZDtmvWratCl27dqFCxcuYPPmzRJRZFlbW2Ps2LH4559/KlxbuXIljIyMKl3DdufOHfj6+jb4ojgoKIjtCKzh2trD71VaWlrlEWpftysuLhZDovqRm5uLuXPn4t69e7Vqz/Wi+M2bN1iwYAHy8vIwZMgQWFpaQldXF3w+H0lJSfD19UVYWBiWLFkCT09Pzi5Pys3Nxd69e5GdnY0TJ04I3w8ICMCyZcvw5s0brFixAqqqquyFJKQKnTt3RlRUFLKysoSdNx06dEBsbCyKioogJycHAEhOToaioiKbUevkezp1GIbBnj17RJim7qgoJqz54Ycf4Ovri4CAAERERJRbZ9C3b1+MHj1a+MHAJePHj8fjx48hEAigoKAAa2trjB8/Xrh2RJItXLgQHTp0wLJly4Tv2dracnqt9NeqWuvl4eGB0tJSThdXXN7Qo67++usvMAyDuXPnQk1NrU4b3EjC2r02bdrgwYMH+PTpU4VN4sp8/PgRDx8+5PTSjh07diA2NhZNmjRB9+7dqz2OSRIcOXIEeXl5+OOPPzB37txy13r27AkrKyscOnQIO3fuxKlTpzj5fZybm4vJkycjISGhwvdmfn4+SktL4eHhgQcPHuD8+fOcKiqqcv/+fTx79gwFBQXg8/lVtuP67umNhZWVFcLDwzFx4kQsXboUo0ePhqmpKa5fv461a9di7ty5CAgIwOPHj2FkZMR23FoLDAz85nsbwucyFcWEVdLS0hg5ciRGjhxZZZucnByoqKiIMdX3efToEeTl5TFr1izMnj0bCgoKbEcSm4iICLx//57tGKSevH37ttz6RC7O4ihTtqHNxIkToaamVqcNbiShKB45ciR2796NjRs3YuPGjZCRqfg4sGXLFuTk5HD6nPjw8HAoKSnB09OT08V9bYWHh6Nt27YVCuIvzZs3DxcvXkRwcDAnv4+PHTuGhIQEmJmZYe3ateWuWVtbw9TUFOvWrUNAQAAOHz5c5b4lXFBQUIBFixZVeZRYGUk5UqyxsLCwwLVr1+Dn54crV65g9OjRGD9+PA4dOgRvb294e3sD+PyzZsaMGSynrb3v2T29IaCimLDC3Nwcw4cPx59//lltu+XLlyMiIgLXr18XU7Lvp6qqiuzsbOzfvx/e3t4YPXo0bGxs0K5dO7aj1TtpaWk0a9aM7RhExC5evIjDhw8jKSmp3Pt6enqYNm0aJk6cyFKyb7dw4UIwDCM8NqzsdWMxffp08Hg8eHh4ICoqCr169QIAJCQk4L///kNwcDDi4+Ohq6uL6dOnsxv2O7x79w4mJiaNoiAGgPT0dJibm9fYrkuXLhU2qOKKoKAgaGtrY9euXZXujK+qqort27dj6NCh8PPz43RRfPjwYdy4cQMyMjLo1asXNDU1K+3AItzCMAx27twJKysr4UakCgoKOHXqFDZu3IiYmBg0b94cs2fPxrBhw1hOW3s2NjblXj979gx6enqcOcqR/mcRVrx58wbv3r2rsV1SUhJyc3PFkEh0wsPDERAQAHd3d0RGRuLQoUM4fPgwjI2NMX78eIwaNYrtiPXG1tYWzs7OuHnzJgYMGMB2HCICW7ZswZkzZ4TLAXR0dFBaWorU1FQ8f/4cGzZsQHx8PNatW8d21DpZvHhxta8lnaKiIk6ePInly5cjMjJS2OHx6NEjPHr0CMDnY0J27NjB2TOKAaBdu3blzl+WdIqKirX62fru3bsqp803dG/evMGgQYOqPSpOTk4OPXr04PxmlpcvX4asrCzOnj2L7t27sx2HiNiQIUPKve7QoQNOnjzJTph6sHDhQsjIyODy5ctsR6kVKoqJWMyaNQuvXr0q915gYGC1Pdp5eXnIzc1F+/bt6zmdaMnKymL06NEYPXo0Xr9+DXd3d3h4eODOnTuIiorC5s2bOdNrVlf6+vrQ1dXFrFmzoKenh86dO0NVVbXSr1cSpqBKuuDgYJw+fRotWrTA+vXrYW5uLvy3LCkpQUBAAJycnHDu3DmYmZlx+kzqvXv3wsDAAEOHDq22nbu7O+7evSsR599qaGjg5MmTePDgQaX7OvTu3ZvtiN9typQp2LBhA6KiohrFvg7dunXDrVu3EBcXV+7c6S/FxcUhOjpaeOoD1ygpKdWqoyMvL4/zy5dSUlLQt29fKog5ztPT87vu5+oGgKmpqfjpp5/YjlFrVBQTsbC3t8e8efOErxmGwcePH/Hx48dq71NQUCi3aRPX6OrqYunSpfj9998REhICNzc3hIeHo7S0FAzDwNzcHJaWlrC0tISenh7bcb+bg4ODcF3m8+fP8fz58yrbUlHc8Lm4uEBGRgZHjhxB586dy12TkZHBqFGj0KZNG9jZ2eHs2bOcL4qtrKxqLIpDQ0MRHh4uEUVxme7du0vsQ7ednR3i4uIwc+ZM2NraokuXLsIp85WpzdTjhmzy5Mm4ceMG5syZg/Xr18PMzEzYkcXn83Ht2jU4OjqCz+dj0qRJLKf9Nt26dUN4eDgePnyIbt26Vdrm2bNniIqK4nzHjoqKisR2ojcmZc9G34qrRbGOjg5SUlLYjlFrVBQTsRg8eDC8vb3B5/MhEAhgY2MDU1PTatf6yMvLo2XLlmjSpIn4gtYTKSkpmJmZwczMDOnp6bh48SIuXbqE5ORkHDp0CIcOHULnzp0xduxY/PLLL2zH/WaNbV2mpHvw4AF+/PHHCgXxl7p27QojIyPcv39fjMm+39GjRyscExYfH4+9e/dWeU9ubi7Cw8MlYjfbLyUmJuLmzZtIS0uDjIwMdHR0MGjQIE5vpFYmPz8fiYmJKCoqwrlz52ps/+TJEzGkqj9Dhw6FnZ0d3NzcsHjxYsjLy0NHRwfA51HHT58+QSAQwNbWllNrFb80ZcoUhISEYPbs2Vi2bBmGDh0q7OjIyclBcHAwtm/fjtLSUk5vEgcAJiYmCA4OxocPHzi9jIF8JicnB2NjY84uXairtWvXYuHChfjf//6HX375BZ07d27Qa+IbbjIicTp16iT8tY2NDYyMjKqc3iXJNDU18euvv+LXX39FREQE3NzcEBgYiMePH+PJkyecLoob27pMSZefnw91dfUa26mrqyM2Nrb+A4nQx48fsX//fmEnDsMwePr0KZ4+fVrlPWU7U3P52K0vJScnY8OGDcKdbct2sAU+/31YWlpi5cqVnD7rddu2bbhx4wYYhkGHDh2EZ4JKso0bN6JLly44duwYkpOTkZCQILzWunVrzJw5E5MnT2Yx4ff56aefMGvWLBw7dgzr1q3DunXr0KRJEzAMI+zoEggEmDZtWo0zPxq6JUuWIDQ0FMuXL4ejoyO0tLTYjkS+wZAhQ3D9+nUUFhYiNjYWw4YNg6WlJQYMGCDRAwnHjx9HixYtwOPxwOPxAHxe/lDVkrpbt26JO2L5DILanD9BCKlX2dnZ8PT0xIULF+Dr68t2HJEpLS1FdnY25OTkJKKX28DAAPr6+pWOsOzdu7fKa1evXsWzZ884Nwplbm4OGRkZ+Pv7V9tuxIgRKC4uxrVr18SU7Pt9+vQJ//33n/DMz9OnT0NPTw8DBw6stD3DMJCXl0f79u1haWkJaWlpccYVuczMTEyYMAGpqalQVVWFiYkJdHR0IBAI8Pr1a9y4cQO5ubno2rUrzp49y9kZOyYmJigoKMCZM2fQpUsXtuOIXVpaGtLS0gB87pDV1tZmOZHoBAcHw9nZGXfu3EFRURGAz8s6DA0NYW9vjxEjRrCc8PutXbsWiYmJuHPnDgCgZcuWVZ61zTAMLl26JO6IpJZycnJw5coV+Pr6IioqCsDnDmULCwtYWlqia9euLCcUvboOfMXFxdVTktqhopiwig6kl0wRERE4cuQIoqKiUFxcDODzBmT9+vXDtGnTMGjQIJYTfhsDA4Mqe3XLPkoru142Ase1onjdunVwd3fHH3/8gTlz5lTa5ujRo9i+fTsmTJiATZs2iTmh6BgYGMDKygpbt25lO4pYODk5wdnZGRYWFnBycqownS8/Px8ODg4IDAzE0qVLqz33tiEzNDRE//79cfDgQbajkHqUnZ2NkpISqKqqNujpmXVVl6KCiz9jGqu3b9/Cx8cHvr6+iI+PB8MwaNeuHaysrGBhYSExR8i9efOmTu1btWpVT0lqh4piwoq6HkhPH/TcceLECWzbtk3YySEvL4/S0lJhccwwTLVFVkO2cuXK77qfawfbJycnw8rKCp8+fcKQIUNgYWEBXV1dAMDr16/h6+uL4OBgyMvLw8vLC23atGE5sWhkZmaiRYsWwtdpaWl48eIF+vXrJzFT3czNzcHn83H16tUqj7YpLCyEubk5VFRUhFPfuGbs2LFo2rQpnJ2d2Y4iVs+fP8eTJ0+Qm5uL0tLSKttRh3PDdvv27Tq179OnTz0lIfUlISEB3t7e4PF4eP36NRiGgaGhIaysrDBq1KhqNwYkokVFMWHFrl27cODAgVofSM+1YqKxio6OxtSpUyEvL4/ff/8dFhYWwuIiLS0NPj4+2LdvH4qKiuDi4oKePXuyG5jU6MaNG/jtt9+Qn59foSAUCARQVlbGjh07OHXsQlUePnwIBwcHqKqqliuifH19sXz5crRp0wbbt2+XiJ2ae/ToATMzM/z333/Vtvvtt98QFhbGuTXjZVxdXbFx40acOHECffv2ZTtOvSsqKsJvv/1W4/m81OFMSMMTHR0NX19f+Pv7IysrC9LS0jAxMWkUM10iIiLQv39/VjNIzhwTwil0IL1kOnHiBABg3759GDBgQLlrWlpamD17NgwMDDB79mw4OztzvihOSEiQiKO0qjNw4EAEBATA1dUVd+7cQXp6OgQCATQ1NdGnTx9MnDixVptxNXQvX77EtGnTUFBQUOH7Ul1dHQYGBnjy5AlmzpyJS5cucX56m46ODhITE2tsl5mZyeldqIcOHYqwsDDMnj0blpaW6NmzJ1RUVKrshOX6kUwHDx5ESEgIpKSkYGRkhJYtW0rUdOLGLjExESkpKVBRUUGXLl2Qm5uLZs2asR2LiIiRkRF69uyJwYMHY+vWrUhISKixg6uhi46OhrOzM968eYPi4mJ8ORbL5/NRWFiIrKws5OXl4fHjxywmpaKYsIQOpJdM0dHR6NGjR4WC+EsmJibo0aOHcKMJLhszZgyMjIwwYcIEjBo1SmKPWVBTU8OCBQuqbZOTkwMVFRUxJRK9AwcOoKCgAMuWLaswtb9///7w8PDAsWPHsG3bNuzfv5/zs1emTp2KzZs3w8XFpcpja8LCwhAdHY0//vhDzOlEx8TERHh2uoeHBzw8PKptz/WRUx6PBxkZGZw5cwY//vgj23GIiLi4uODo0aN4+/YtAMDS0hJbt27FokWL0KRJE2zZsoXTnVcEiIyMxOXLl3H16lXk5ORAIBBARUWF0xvG3b9/H7/88gtKSkpQ1cTkshlo+vr64oxWKSqKCSvoQHrJlJOTU6s1Ta1ateL8wycA6OnpITo6GjExMdi8eTPGjBkDW1tbiensMTc3x/Dhw/Hnn39W22758uWIiIjA9evXxZRM9KKjo6Gvr1/tWvdZs2bBw8OD019nmcGDB+PWrVtwcnLCnTt3YGFhgXbt2kFKSgppaWkIDg7GuXPnoKOjg3bt2iEoKKjc/VwZUe3duzfbEcQqNTUVvXv3poJYgjg4OMDLywsCgQBqamrIysoSXnv79i2SkpLw888/48KFCxJxykNjEhMTAx6PB39/f2RmZkIgEEBBQQGjR4+GpaUlTExMOD3T4+jRoyguLoa5uTnGjRuHsLAwuLu7Y+/evSgtLUVoaCguXryIH374Ae7u7mzHpaKYsIMOpJdMampq5c7ErMqLFy84ffZpGR6Ph3v37sHd3R1+fn44f/483NzcoK+vD1tbW1hZWXF6atubN2/w7t27GtslJSUhNzdXDInqT0ZGBrp161ZjOz09vQoFIhcNHz5cOILq7+9f6bFbAoEAqamp+P333ytc40qn1pkzZ9iOIFbNmjWDgoIC2zGIiHh5ecHT0xMdO3bEli1b0L1793I7Up8+fRorVqzAnTt34OLigvnz57OYltTG48ePwePx4Ofnh9TUVAgEAsjIyMDU1BQWFhYwNzeXmFlnMTExaNGiBXbu3Ck8mvP8+fOQkpKCmZkZhg0bBn19fWzZsgVnz57FL7/8wmpeKooJK+hAesnUp08f8Hg8eHt7w8rKqtI2Pj4+iI+Px5gxY8Scrn4YGhrC0NAQq1evhp+fHy5cuIDo6Gg4OTlh27ZtGDZsGGxtbdGvXz+2o9Zo1qxZePXqVbn3AgMDqx0VzMvLQ25uLtq3b1/P6eqXurp6rTp0kpOTJaJDp7GMoObn58Pf3x+PHj0STj3V1tZG586dMXLkSCgrK7OcULQGDRqEgIAA5OXlSdzX1hi5ublBQUEBR48erfQ5SVtbG/v378eQIUPg7+9PRXEDlZCQAB6Ph8uXLyMxMRECgQBSUlLo3bs3LC0tMXz4cE4vP6rK+/fvMWDAAMjJyQEAOnbsCAB49OgRTE1NAQA///wzjh49Ch6PR0UxaZz27duHjh07IjQ0FKampnQgvYSYPXs2rly5gpUrV+LBgwflzttLTk6Gr68vzp07BxkZGcyePZvltKKloKCAcePGYdy4cUhKSoKfnx+uXr0KHo8HHo+H1q1bY8KECZgwYUKDLars7e0xb9484WuGYfDx40d8/Pix2vsUFBSwbNmy+o5XrwYMGICLFy/i4sWLGD9+fKVtLl++jMePH0tEh46kj6Dy+Xzs3bsXJ06cwKdPnwBUPEt88+bN+Pnnn7FkyRJIS0uzllWUFi9ejJCQECxatAh//fUXWrZsyXYk8h3i4uJgbGxc7cCBsrIyjIyMEBMTI8ZkpC7GjBkjnJnTrVs3WFhYYMyYMRK/DlxOTq7cqHfz5s2hpKSEFy9eCN9jGAZdu3bFnTt32IhYDhXFhBVfrx1ISUlBSkpKpW0l5VzQxsDAwACbNm3CmjVr4OzsXOFs0LJpQo6OjujcuTNLKeufvLw8lJSUoKCgAIZhwOfzkZSUhH///Rf79+/Hr7/+irlz57Ids4LBgwfD29sbfD4fAoEANjY2MDU1xZIlS6q8R15eHi1btkSTJk3EF7QeTJ8+Hb6+vli7di0iIyMxdOhQtGzZEgzD4O3bt7h27Rq8vb0hKyvLyTO2vybJI6hFRUWYOXMm7t69C4FAAC0tLfTu3RsaGhqQkZFBRkYGYmJikJiYiKNHj+LOnTs4efIkJ6cs2tjYVHhPWloat27dgpmZGbS1tdGsWbNK9/CgDueGr7S0tFYdNqWlpSguLhZDIvI9VFRUUFpaCi8vL3h5edXqHi7/P23VqlW5AhgA2rZti7i4uHLvle1CzTYqigkrTp8+Xeu2GRkZ9ZiEiJqNjQ26d++OEydOVHqEj729fYPYZVDUioqKEBgYiEuXLiEiIgJ8Ph8A0LdvX9jZ2aFXr17g8Xg4evQodu7ciZKSkhp3dGZDp06dhL+2sbGBkZFRuTVskuqHH37A1q1bsWrVKvj4+MDX17fcdYFAAHl5eWzZsoXTfx+NYQR106ZNiIqKgq6uLtatW1flGdoRERFYv3497t27BycnJzg5OYk56feraW13amoqUlNTK71GHc4NX5s2bfDgwQN8+vSpyk6bjx8/4uHDh5w/Jk7SCQQC5OTkICcnp073cfn/ab9+/eDs7Ixjx45hxowZkJKSQteuXYXLzIyMjJCVlYW7d+82iFktjKCqPbIJYVlUVBRcXFwQGBiIBw8esB2HkErdv38fly5dgp+fH3JzcyEQCNCiRQvY2NjAzs6uwoPKkydPYGNjAw0NDYSHh7OU+tuUlpYiOztbuGGGJEpPT4enpydu3bqF9PR0lJSUQENDA7169YKdnR1atmyJjIwMTk57q8sIKsMwMDQ05NwI6r179zBx4kTo6uriwoULNS5VyMnJgZ2dHZKSknDp0iXOzWC5ffv2d91fm9MCCHv279+P3bt3Y9y4cdi4cSNkZGRgYGAAKysrbN26FQCwZs0aXLx4EQsWLMDixYtZTkwq01j/n6akpMDS0hIfP37E0KFDsWfPHjx48AATJkxAs2bNMGDAAMTExCA9PR2TJ0/GunXrWM1LRTFpUAoKCuDl5YWzZ8/i2bNnEAgEYBiGMzudNnb29vYYOHBguXWplfnrr78QEhJS6Y63XDJmzBi8ePFCuGnGgAEDMHHiRAwZMqTaYxQMDQ0hJSXFmTVgEREROHLkCKKiooRT9GRlZdGvXz9MmzYNgwYNYjmheHC9o27t2rVwd3ev9QhqcnIyxo8fz6kR1LIC4fjx4+jfv3+t7rl9+zbs7e0xbdo0rF69up4T1p/Xr1/j3bt30NHR4WSnDano48ePmDBhAl68eIHWrVujV69e8PDwQNeuXTFo0CAEBwcjPj4eurq68PDwkNjOSsJd9+7dg6OjI7p06SL8WfLPP//gxIkTwjY6Ojpwd3eHuro6WzEBUFFMGoiXL1/i7Nmz8PT0RF5enrAYNjY2hq2tLcaOHct2RFILX/dgV2X69Om4e/cuJwuLLxkYGEBLSwvjxo3DhAkToKOjU+M9hYWF2Lx5M7p16wY7OzsxpPw+J06cwLZt24TTweXl5cutX2MYBn/88YdErLOtjKR01DWWEdSRI0eipKQEgYGBdbpv2LBhUFJSgqenZ/0Eq0dxcXFYvXo1Hj9+LHxvwIAB2LRpU60+k0jDlpGRgeXLlyMyMrLS6927d8eOHTto+jRp0L5eAnD37l1ER0ejefPmGDVqFJSUlFhM9xkVxYQ1AoEA165dg4uLCyIiIoTvaWlpwdraGra2tvQh38AtW7as3Jrv27dvo0WLFujQoUOV9+Tm5iI+Ph46OjqcP+81ODgYgwcPrnQTG0kQHR2NqVOnQl5eHr///jssLCzQokULAEBaWhp8fHywb98+FBUVwcXFBT179mQ3sAhJWkddYxlBNTQ0RL9+/XDo0KE63bdw4ULcuXPnu6c5iltqaiqsra0rXaeoo6MDT09PTp+VTv7PgwcPEBERgdTUVPD5fGhoaKBv376N5ng1wm2ZmZnC5wfg8zPEixcv0K9fvwazbpo22iJi9/79e7i7u8PV1VV4cLmsrCzk5OTw8eNHhIaGsh2R1NKgQYPg4OAgfM0wDDIzM5GZmVntfQzD1DjFuiGqbBTJ29u71vdbW1uLLowYlE1v2rdvHwYMGFDumpaWFmbPng0DAwPMnj0bzs7OnC+KJbmjLioqCq1atap1QQx8XsfWunXrBnFURm1JSUl9007o0tLSwo3HuOTEiRPIyclBnz59sGrVKrRr1w7x8fHYuHEjHj9+jLNnz9LZtRKie/fu6N69O9sxCKmThw8fwsHBAaqqquVOJLlz5w6WL1+ONm3aYPv27Q3ie5uKYiI29+/fh4uLC65cuYKioiIIBAJ06NABEyZMgLW1Nf74448qpweRhsna2lp4xAAALFq0SLjDdGUYhoG8vDzatWuHVq1aiTOqSDg4OHxXjybXiuLo6Gj06NGjQkH8JRMTE/To0QNRUVFiTCZajaGjLjU1Ff369avzfZ06deJUUayurl7hCJDaePnyJTQ1NeshUf2KiIhA8+bNcfjwYeHURENDQxw5cgSmpqYICwujolhCJCYm4ubNm0hLS4OMjAx0dHQwaNAgWj9OGqyXL19i2rRpKCgoqNBprq6uDgMDAzx58gQzZ87EpUuXWO90pqKYiMX48ePx+PFjCAQCKCgowNraGuPHj4exsTHb0ch3GjJkiPDXvXv3xsCBAzF06FAWE9U/OTk5GBsbc2pX3m9RNgJVk1atWnFujS3QuDrqGssI6o8//ghfX1/ExcXV+uisuLg4PHv2DJaWlvWcTvRSU1Mr/SxSU1ODoaEhnj9/zlIyIirJycnYsGEDbt68CQDCpRzA545mS0tLrFy5ssZ9AggRtwMHDqCgoADLli2rsO9I//794eHhgWPHjmHbtm3Yv38//vrrL5aSfkZFMRGLR48eQV5eHrNmzcLs2bOhoKDAdiRSD86cOcN2hHo1ZMgQXL9+HYWFhYiNjcWwYcNgaWmJAQMGNJg1MaKkpqaGhISEGtu9ePGCcw9kja2jrrGMoFpYWMDHxweOjo5wdnau8ZzloqIibNiwAQzDwMbGRkwpRaegoKDKDWo0NDQ4s8M9qVxmZibs7e2RmpoKVVVVmJiYQEdHBwKBAK9fv8aNGzfg5eWF58+f4+zZs9/U8UVIfYmOjoa+vn61G3HOmjULHh4euH79uhiTVY6KYiIWqqqqyM7Oxv79++Ht7Y3Ro0fDxsYG7dq1Yzsa+Q5lG2UNGDAACgoKdd44y9zcvD5i1ZsDBw4gJycHV65cga+vL7y8vODl5QV1dXVYWFjA0tISXbt2ZTumyPTp0wc8Hg/e3t6wsrKqtI2Pjw/i4+MxZswYMaf7Po2to66xjKAOHjwYffv2xe3btzF37lxs2bIFWlpalbZNTU3FihUrcO/ePQwdOrTaZQINVWlpaZWFv7S0tHBpC+GmgwcPIjU1FRYWFnBycqowIyA/Px8ODg4IDAzEqVOnMHfuXJaSElJRRkYGunXrVmM7PT29BrHxKu0+TcSiuLgYAQEBcHd3R2RkZLmdXMePH49Ro0Zh/vz5iIyM5OQ0zMbKwMAADMPg8uXLaN++vfB1bXH93/rt27fw8fGBr68v4uPjwTAM2rVrBysrK1hYWLC+PuZ7xcXFwdbWFgKBAFOmTCn3NSUnJ8PX1xfnzp0DALi7u3Pm2B4A6NevH7Kzs8EwDHR1dSvtqJsxY4bEfCaFhoZi3rx5+PHHH2s9gmpvb4979+7h2LFjnCoYs7KyMGnSJCQlJUFGRgb9+/dHjx49oK6uDllZWbx//x4xMTEIDw9HSUkJ9PT04Obm1iCOBKmr6o7BW758OXx9fSXi+7exMjc3B5/Px9WrVyErK1tpm8LCQpibm0NFRQU8Hk/MCQmpmpmZGZSUlODj41Ntu3HjxiE9PZ310WIaKSZiISsri9GjR2P06NF4/fo13N3d4eHhgTt37iAqKgqbN2+W2GNtJFnZURBlo2yN7WgIbW1tzJkzB3PmzEFCQgK8vb3B4/Gwa9cu7N69G4aGhrCyssKoUaPQvHlztuPWmYGBATZt2oQ1a9bA2dm53M6RwOe1bTIyMnB0dORUQQwA4eHh5TrqDh06hMOHD5frqJMkjWkEVU1NDe7u7li9ejUCAwMRHh5e4WGrbDzAxsYGa9as4WRBTCRfRkYGzMzMqiyIAaBJkyYwMjJCWFiYGJMRUrMBAwbg4sWLuHjxIsaPH19pm8uXL+Px48cNYrYZjRQT1vD5fISEhMDNzQ3h4eEoLS0FwzDQ0dGBpaUlLC0toaenx3ZMQuosOjoavr6+8Pf3R1ZWFqSlpWFiYoKDBw+yHe2bPH/+HCdOnMCdO3eQnp4OgUAATU1N4U7j+vr6bEf8Ll921KWnp4NhGCgrK0NKSgq5ubkSM9LWmEZQy8TFxcHf3x/37t1DRkYG+Hw+tLW10a1bN1hbW6N9+/ZsR/wuNFIs2UaOHAkFBQV4eHhU227KlCnIyMhAQECAmJIRUrPnz5/D1tYWRUVFGDNmDIYOHYqWLVuCYRi8ffsW165dg7e3N6SkpODu7l7rpT31hYpi0iCkp6fj4sWLuHTpEpKTk4VTcDt37oyxY8fil19+YTkhIXXD5/MRHh6OrVu3IiEhAQzD0MNpA9cYOupycnKEI6gAKix3oBFUbjEwMICiomKlM1Hev3+PgoIC6OjoVHovwzDC7wPSMJ05cwabN2/G2rVrMXXq1ErbhIWFYe7cufjjjz9oTTFpcK5evYpVq1YhLy+v0p838vLy2LJlC0aPHs1Swv9DRTFpcCIiIuDm5obAwEAUFxdTMdGA5eXlfdf9ysrKIkrScERGRuLy5cu4evUqcnJyIBAIoKKighEjRmDjxo1sxyO1JOkddZI+gtpYfM/ICv1sbfiSkpKwdetWBAUFYcSIEbCwsEC7du0gJSWFtLQ0BAcH49y5c9DQ0ICDg0OFvQK4tpklkUzp6enw9PTErVu3kJ6ejpKSEmhoaKBXr16ws7NDy5Yt2Y4IgIpi0oBlZ2fD09MTFy5cgK+vL9txSCW+Zx0pwzB4/PixCNOwJyYmBjweD/7+/sjMzBQe82NmZgZLS0uYmJhARoabWzh8+PAB3t7eiIuLQ25ubpW72TIMgz179og5nXhQRx1pqG7fvv1d99fmHHLCnrLNK788m/hr1V2jzylCao+KYkLIN6tplEJWVhba2tqQlpZGRkYG8vPzAQAtWrSAoqIirl69Ko6Y9eLx48fg8Xjw8/NDamqqcNOpQYMGwcLCAubm5hWOz+CatLQ0TJw4EWlpaajpR0VjKBSpo44QIk7Tpk37rvvPnDkjoiSESD4qigkhIpObm4upU6fiw4cPWL16NYYMGVJuhDQiIgKOjo4QCARwdnaGhoYGi2nrLiEhATweD5cvX0ZiYiIEAgGkpKRgbGwMS0tLDB8+HCoqKmzHFJmVK1fCw8MD2traGDduHFq2bFntiLeNjY0Y0xFCCCGkoSktLUVISAiaNWtW7lSSkpIS7N+/H56ensjOzoaBgQHmzJmDIUOGsJj2/1BRTAgRmU2bNuHChQvw9vZG27ZtK22TmpqK0aNHY9iwYZXumNqQfTmVrVu3brCwsMCYMWM4V9zX1qBBg1BUVAQ/Pz+oqamxHYcQQhql9+/f4/z584iMjER6ejrk5OSgrq6O/v37Y+zYsRL7M4hwz/PnzzFv3jykpKRg3Lhx2Lx5s/DaggULEBwcXG7mGcMwcHBwaBD7dHBzkRshpEHy9/dHnz59qiyIAaBly5bo27cvp89UVFFRQWlpKby8vODl5VWrexiGwaVLl+o5mWhlZ2fDxMSECmJCCGHJ7du3sXjxYuTm5lZYxnLz5k0cPXoUO3fuRP/+/VlKSMhnHz9+xPTp05GZmQltbe1y+85cvnwZ165dA8MwWLBgASZOnIiEhASsXr0a27Ztg4mJCeunO1BRTAgRmfz8fMjKytbYjs/no7i4WAyJRE8gECAnJwc5OTl1uq+qjVAaMh0dHRQUFLAdgxBCGqU3b95gwYIFyMvLw5AhQ2BpaQldXV3w+XwkJSXB19cXYWFhWLJkCTw9PRvMLr6kcXJ1dUVmZiYsLCywZcsWyMnJCa85OzsDAExNTfHbb78BALS0tLBjxw5MnjwZrq6uWL16NSu5y1BRTAgRmbZt2+LWrVt49+4d1NXVK23z6tUrREZGokuXLmJO9/1Onz7NdgSxGj16NI4cOYKEhATWe3AJIaSxOXLkCPLy8io9g7hnz56wsrLCoUOHsHPnTpw6dQoODg4sJSUECA4OhpKSEtavX1+uIM7NzUVsbCwYhsH48ePL3fPjjz+iXbt2uHnzprjjVkBrigkhIuPs7AwnJyd07NgRGzZsQK9evcpdDw0NhaOjI1JTU7F161ZYWlqylJRU5utzp/Pz8zFr1izk5eVhyZIl6NWrF1RUVCAlJVXp/ZJ47jQhhLDF3NwcMjIy8Pf3r7bd8OHDwTBMje0IqU8DBw5E+/bthaPCZYKDg/Hrr79CWloat27dqvCssHDhQkRGRuLu3bvijFsBjRQTQkRmypQpuHHjBoKDg/Hzzz9DWVkZWlpaEAgESE1NRUFBAQQCAezs7KggboC+3CXyaytXrqz2Xkk6d5oQQhqC9PR0mJub19iuS5cuCA4OFkMiQqqWk5NT6aZvZcVup06dKu08l5aWRklJSb3nqwkVxYQQkZGSksK+fftw7tw5uLi44MWLF/jw4YPweqdOnTBz5kxYW1uzF5JU6XsmDtGkI0IIES1FRUW8e/euxnbv3r2DvLy8GBIRUjVlZeVK9yGJiooCwzAVZg+WSU1Nhaqqaj2nqxkVxYQQkZKSksLUqVMxdepUZGRkIC0tDQCgra2NFi1asJyOVCcuLo7tCIQQQv6/bt264datW4iLi4OBgUGlbeLi4hAdHY1+/fqJOR0h5bVv3x5Pnjwp915WVhbu378P4PP06q9lZmbi0aNH1c5UE5fKF4YRQogIaGhooHPnzujYsSMVxIQQQkgdTJ48GSUlJZgzZw4CAwPB5/OF1/h8PgIDAzFnzhzw+XxMmjSJxaSEAEOGDEF6ejoOHz4sfO/QoUPg8/lo1qxZpUXx9u3bIRAIKr0mbrTRFiFE5OLi4nD8+HFERkYiIyMDVlZW+Oeff7Bq1Sq0b98eM2fOhLS0NNsxSRWysrIQEhKCzMxM6Orq4qeffqJNtAghhAXr1q2Dm5sbGIaBvLw8dHR0AAApKSn49OkTBAIBbG1t4eTkxHJS0tjl5+djxIgRePfuHQwMDMAwjHDk2MHBAb/88gsAoKSkBA8fPsSpU6fg5+cHFRUVBAQEoFmzZmzGp+nThBDRcnd3h6OjY7lNE8r63mJiYuDh4YF79+5h9+7dVe5iTNhz6dIlbNy4EYWFhcL3VFVV8ffff2Pw4MEsJiOEkMZn48aN6NKlC44dO4bk5GQkJCQIr7Vu3RozZ87E5MmTWUxIyGdKSko4ceIEFi1aJCyGGYbBlClThAUxANy5cwczZ86EQCBAkyZNsH37dtYLYoBGigkhIhQbG4spU6ZAUVER8+fPh6mpKSwsLGBlZYWtW7fi6tWrcHJyQkZGBv7++2+MHTuW7cjkC2X/fnw+H2pqamjVqhVevXqFDx8+QF5eHh4eHmjfvj3bMQkhpFFKS0sT7tOhqakJbW1tlhMRUhGfz0d0dDQyMzPRuXNntG3bttz1+Ph4zJw5E8bGxliwYAH09fVZSloejRQTQkTmyJEjAICDBw/C2Ni4wvXhw4dDT08PVlZWcHd3p6K4gXF2dgafz8ecOXPw+++/Q0ZGBsXFxdi4cSPc3d3h4uKCNWvWsB2TEEIaJS0tLWhpabEdg5BqSUlJVfoMWEZfXx83btwQY6LaobmLhBCRiY6ORs+ePav9MNTT00OvXr3w6tUr8QUjtRIbG4u2bdti2bJlkJH53GcqKyuL9evXQ1VVFVFRUSwnJIQQQggRPSqKCSEik5eXV6tdpps2bYrc3FwxJCJ1kZmZiU6dOlV4X0ZGBt26dcObN29YSEUIIYQQUr+oKCaEiIympibi4+NrbBcfHw8NDQ0xJCJ1UVRUBHl5+Uqvqaio4OPHj2JORAghhBBS/6goJoSIjImJCZKSkuDq6lplm7Nnz+L169cN4kw6Uh6fzwfDMJVek5KSKndGJiGEEEKIpKCNtgghIjNv3jzweDw4OjoiOjoa/fv3BwDk5OQgIiICQUFBcHV1hYKCAmbNmsVyWkIIIYQQQqgoJoSIkI6ODg4cOIDffvsN3t7e8PHxAcMwCAsLQ1hYGAQCAZSUlLBjx44KW/QTQgghhBDCBiqKCSEi1bt3b/j7+8PNzQ2RkZFITU0Fn8+HhoYG+vTpg4kTJ9J64gasoKAAKSkplb4PAKmpqajqeHsdHZ16zUYIIYQQUh8YQVVPN4QQQhoVAwODKtcU14RhGDx+/FjEiQghhBBC6h+NFBNCCBH61n5S6l8lhBBCCFfRSDEhRKSePn0Kd3d3vHr1CoWFhVW2YxgGp06dEmMyUp/S09OhqanJdgxCCCGEkDqjkWJCiMjExsZi2rRpKCkpqXHk8Fun6ZKGJSoqCi4uLggMDMSDBw/YjkMIIYQQUmdUFBNCRGbHjh0oLi6GiYkJRo0aBRUVFbYjkXpQUFAALy8vnD17Fs+ePYNAIKBODkIIIYRwFhXFhBCRiY+PR/v27XHkyBEqkiTQy5cvcfbsWXh6eiIvL09YDPfu3Ru2trZsxyOEEEII+SZUFBNCREYgEOCHH36ggliCCAQCXLt2DS4uLoiIiBC+p6WlBWtra9ja2qJ169YspySEEEII+XZUFBNCRMbIyAhxcXE0nVYCvH//Hu7u7nB1dRWeTSwrKws5OTl8/PgRoaGhbEckhBBCCBEJKbYDEEIkx5IlS5Ceno7NmzejqKiI7TjkG9y/fx9//vknTE1NsXPnTqSkpKB9+/b4888/ERYWhh49erAdkRBCCCFEpGikmBAiMgYGBtiwYQNWrlwJX19f6OnpoXnz5pW2ZRgGe/bsEXNCUp3x48fj8ePHEAgEUFBQgLW1NcaPHw9jY2O2oxFCCCGE1BsqigkhInPv3j04OjoCALKzs3H37t0q29L06obn0aNHkJeXx6xZszB79mwoKCiwHYkQQgghpN5RUUwIEZl///0Xnz59Qrdu3TBy5EioqalR8cshqqqqyM7Oxv79++Ht7Y3Ro0fDxsYG7dq1YzsaIYQQQki9YQQCgYDtEIQQydCrVy+0aNECPB4PMjLU58Y1xcXFCAgIgLu7OyIjI4UbphkbG2P8+PEYNWoU5s+fj8jISDx58oTtuIQQQgghIkFFMSFEZPr06YN+/fph9+7dbEch3+n169dwd3eHh4cH0tPTwTAMlJWVISUlhdzcXCqKCSGEECIxqCgmhIjM/Pnz8fz5cwQEBNC0aQnB5/MREhICNzc3hIeHo7S0FAzDQEdHB5aWlrC0tISenh7bMQkhhBBCvhkVxYQQkXny5Ans7Owwbtw4rFq1Ck2aNGE7EhGh9PR0XLx4EZcuXUJycrKw46Nz584YO3YsfvnlF5YTEkIIIYTUHRXFhBCROXLkCG7duoUbN25AXl4e+vr6UFVVrXR9MR3JxG0RERFwc3NDYGAgiouLwTAMTakmhBBCCCdRUUwIERkDAwMwDIPafKxQESUZsrOz4enpiQsXLsDX15ftOIQQQgghdUZFMSFEZDw8POrU3sbGpp6SEEIIIYQQUjtUFBNCCCGEEEIIabSk2A5ACGkcMjIy8PDhQ3z8+JHtKIQQQgghhAhRUUwIEamkpCQ4OjoiJiZG+N5///0HU1NTTJgwAT/99BMuXbrEYkJCCCGEEEL+DxXFhBCRSU5Ohq2tLVxdXYWbaN29excHDx5EaWkpWrZsiY8fP2L16tXlimZCCCGEEELYQkUxIURkjh49itzcXFhaWsLMzAwAcPHiRTAMg19//RXXrl3DiRMnAAAnT55kMSkhhBBCCCGf0UZbhBCRGTZsGADg6tWrYBgGADBw4EBkZWUhJCQEWlpaAIDJkycjOTkZ169fZy0rIYQQQgghAI0UE0JEKD09HZ07dxYWxE+ePMG7d+/Qrl07YUEMAJqamsjOzmYpJSGEEEIIIf+HimJCiMgoKSnh06dPwtfh4eEAgL59+5Zrl5aWBiUlJbFmI4QQQgghpDJUFBNCRKZdu3aIiorCu3fvUFxcDF9fXzAMg8GDBwvbxMTE4N69ezAwMGAxKSGEEEIIIZ/JsB2AECI5rKysEB0dDQsLCzRt2hRJSUnQ1tbGoEGDAACOjo7w9PQEANjZ2bGYlBBCCCGEkM9opJgQIjKTJk3CxIkTkZ2djaSkJDRv3hzbt2+HjMzn/reIiAh8+vQJS5YswZgxY1hOSwghhBBCCO0+TQipB2lpaUhLS4O+vj6aNGkifP/atWvQ19dHq1atWExHCCGEEELI/6Hp04QQkZOTk8PNmzexY8cOpKenQ05ODurq6ujXrx+6d+/OdjxCCCGEEEKEaKSYECJSt2/fxuLFi5Gbm4uvP14YhoGKigp27tyJ/v37s5SQEEIIIYSQ/0NFMSFEZN68eYOxY8ciLy8PQ4YMgaWlJXR1dcHn85GUlARfX1+EhYVBRUUFnp6eaNmyJduRCSGEEEJII0dFMSFEZDZs2ABXV1f88ccfmDt3bqVtDh06hJ07d2L69OlwcHAQc0JCCCGEEELKo6KYECIy5ubmkJGRgb+/f7Xthg8fDoZhamxHCCGEEEJIfaMjmQghIpOeno7OnTvX2K5Lly54+/atGBIRQgghhBBSPSqKCSEio6ioiHfv3tXY7t27d5CXlxdDIkIIIYQQQqpHRTEhRGS6deuGmJgYxMXFVdkmLi4O0dHR6NatmxiTEUIIIYQQUjkqigkhIjN58mSUlJRgzpw5CAwMBJ/PF17j8/kIDAzEnDlzwOfzMWnSJBaTEkIIIYQQ8hlttEUIEal169bBzc0NDMNAXl4eOjo6AICUlBR8+vQJAoEAtra2cHJyYjkpIYQQQgghVBQTQuqBq6srjh07huTk5HLvt27dGjNnzsTkyZNZSkYIIYQQQkh5VBQTQupNWloa0tLSAACamprQ1tZmOREhhBBCCCHlUVFMCCGEEEIIIaTRoo22CCGEEEIIIYQ0WlQUE0IIIYQQQghptKgoJoQQQgghhBDSaFFRTAghhBBCCCGk0aKimBBCCPlOvr6+0NfXh76+PtatW8d2HEIIIYTUARXFhBBCyHe6ePGi8Nc+Pj7Iy8tjMQ0hhBBC6oKKYkIIIeQ7pKSkIDIyEqqqqujZsyc+fvwIHx8ftmMRQgghpJaoKCaEEEK+w6VLl8Dn8/Hjjz/CzMwMAHD+/HmWUxFCCCGktqgoJoQQQr6RQCCAh4cHAOCnn37CqFGjAABPnjxBbGwsi8kIIYQQUlsybAcghBBCuCoyMhKvX7+GlJQUzM3NoaWlhR49euD+/fs4d+4cevbsWeGeadOm4fbt29i3bx/U1dWxZ88e3L9/HwCgp6cHOzs72NjYQEqqfL+1mZkZ3rx5A09PT6SlpeHgwYOIj4+HnJwc9PX1YW9vj6FDh9Ypv76+PgDg/v37CAsLw+nTp/HkyRMUFxejffv2sLa2xtSpUyErK1vh3sTERDg7O+PWrVtISUlBQUEBlJWVoa+vDwsLC4wfPx7S0tLC9rdu3YK9vT2MjIxw6tQpHD9+HF5eXnj9+jWaNWuGgQMHYtmyZdDS0sLr16+xd+9eXL9+HdnZ2dDW1saYMWOwcOFCyMnJVciSmZmJ48ePIyQkBG/evIGUlBQ6dOiAMWPGYOrUqWjSpEmd/l4IIYQ0LlQUE0IIId+obIOtPn36QEtLCwBgYWGB+/fvw8/PD6tWrYKKikql916/fh0XLlwAn89Hp06d8OHDB8TGxiI2NhYhISH4999/Ky0AL168iDNnzkBOTg4dO3ZEZmYmbt26hVu3bmHatGlYs2ZNnb+O//77D8ePH4eioiLatm2L9PR0PHnyBE+ePMG9e/ewc+fOcu0DAwOxdOlSFBUVQVFREa1bt4ZAIMDr16+FWW7duoV///23wp9VWFgIe3t7xMTEQFdXF23atMHLly/h5eWF6Oho/PXXX5g/fz4KCwvRrl07yMjIIDk5GQcPHkRycjJ27NhR7ve7e/cuFixYgOzsbMjKyqJdu3YQCAR49OgRHj58CC8vLxw9ehQaGhp1/nshhBDSOND0aUIIIeQbfPjwAQEBAQAAKysr4fsWFhaQlpZGYWGhcGp1Zc6dO4cffvgB/v7+8PT0RFBQEA4cOABFRUVcvXoVJ06cqPS+M2fOYMCAAQgJCcGlS5cQGhqKTZs2QUZGBmfOnIGvr2+dv5bjx49j3rx5iIyMhKenJ8LDwzF37lwAwOXLl/HkyRNh25ycHKxatQpFRUWYPHkybt68CW9vb/j4+ODGjRuYNm0agM/HVD179qzCn/Xo0SM8ffoUhw8fRlBQEHg8Ho4dOwaGYZCcnAx7e3v06NEDwcHB8PX1RXBwMBYtWgQA4PF4eP36tfD3SktLExbEdnZ2uHnzJnx9fcHj8XD16lUYGhoiLi4OS5YsqfPfCSGEkMaDimJCCCHkG/B4PHz69Any8vIYMWKE8H11dXUMGDAAAODq6lrl/UpKSjh06BBat24tfM/MzAwODg4AgMOHD6OoqKjCfTo6Oti7dy/U1dUBAAzDwM7ODrNmzQIA7N27t85fy5AhQ/DHH38IpxlLS0tjyZIlwlHu6OhoYduoqCgUFxdDQ0MDa9asgYKCgvCaoqIiHBwchNOtnz59WumfN3/+fAwePFj4un///sKp5goKCti9e7dwZJdhGMybN0/4e35ZoB87dgzZ2dkwMzPDpk2b0KxZM+G1Nm3aYP/+/VBWVkZUVBRCQ0Pr/PdCCCGkcaCimBBCCPkGZVOnhwwZAmVl5XLXykaOX758iYiIiErvHz16tHDK9ZdsbGzQpEkT5OXl4datWxWu29nZQUlJqcL7kyZNEv6ZL168qNPXUrZr9pekpaXRtm1bAEBubq7wfXNzc8TExCAwMBAyMhVXYRUWFkJVVRUAUFBQUOmfZ2pqWuG9Vq1aAQCMjIzQtGnTctfk5OTQvHlzACh3BnRgYCCA8iP1X2rRogUGDhwIAAgODq60DSGEEEJrigkhhJA6evbsmXBzrMoKsmHDhkFRUREfP36Eq6sr+vfvX6FNjx49Kv295eTk0L59e8TFxeHVq1cYNGhQre7T0dFB06ZN8eHDB7x69QodOnSo9ddTWXEOAPLy8gCA0tLSSq/FxcUhLi4OycnJSEpKwvPnz/Hs2TMUFxcD+Lw7d2VatmxZ4b2ykWA1NbVK7ym7XvZ75ufn482bNwCA/fv34/Tp05XeV9amrh0FhBBCGg8qigkhhJA6KhslBoBff/212rZBQUHIzMxEixYtyr1f1QZcwOdpyED5Edra3vfhw4dK76tOZbtLf+nr4jY0NBSbN29GYmJiufc1NTUxcuRIhIWFIScnp8rf78sp11/7etftqnw5YlzVNO0vffjwoVa/LyGEkMaHimJCCCGkDoqLi+Ht7Q0AaNasWZUFnkAgQHp6OoqLi3HhwgXMnz+/3PWqphYD/1fwla0bFsV9ohIZGYn58+eDz+ejZ8+esLS0RKdOnaCnpyf8c78e3a4PX/69+/j4oFOnTvX+ZxJCCJFMVBQTQgghdRAaGop3794B+Lxrc/fu3atsa2FhgWfPnsHNzQ1z584tNwpa1ehmQUEBXr58CQD44YcfKlx/+vQpevfuXeH9pKQk5OfnV3mfqBw5cgR8Ph/9+vXD8ePHy51FDABFRUV4//59vf35ZZo1a4YWLVogMzMTz58/r7Iojo+PB5/Ph46OTrWj7IQQQhov2miLEEIIqYMLFy4AAPT19astiAFg8uTJAD6vaw0LCyt3jcfjVTrqe/HiRRQXF0NTUxNGRkYVrl+6dAl8Pr/C++fOnQMA9OzZs9I1u6JSdiSSgYFBhYIYADw9PYVriktKSuotB/B/G3Y5OztX+nfy4cMH2Nvbw9raGqdOnarXLIQQQriLimJCCCGkljIyMhAeHg4AGDduXI3tx44dK1wf/PXxTG/fvsXSpUvLjar6+flh27ZtAIClS5dWur724cOHWLt2rbCg5vP5cHZ2xsmTJ4X31aeyDbx4PB4SEhKE7xcWFsLZ2RlOTk7C9z59+lSvWebOnQtFRUXcvXsXy5cvR1ZWlvDamzdvMHfuXGRnZ6Np06aYOnVqvWYhhBDCXTR9mhBCCKklT09PlJSUQFZWtspjgL6krKwMS0tLnD9/HmFhYUhNTRVe69ixI0JCQjB48GB07NgRWVlZSElJAQDMnDmzyqK7U6dOuHDhAvz8/NChQwe8ffsWGRkZkJKSwsqVK9GvXz/RfLFVWLhwIW7evImMjAxYWlqiXbt2kJOTQ2JiIj5+/Ag1NTXh7tlv376t1yxt27bFf//9h6VLl8LX1xf+/v744YcfUFxcjFevXqGkpASKioo4fPhwva6zJoQQwm00UkwIIYTU0qVLlwB8Pte3qqODvlY2hbq0tBTnz58Xvm9ubo5jx46hR48eSEhIQEFBAX766SccPXoUf/75Z5W/n729PXbs2IH27dsL1yWPGDECrq6umD59+jd+ZbXXrVs3eHl5wcrKCjo6OkhKSkJSUhLatGmD+fPnw9fXF/b29gCAkJCQKo9lEpXBgweDx+Nh+vTpaNOmDV6+fInExES0atUKU6ZMgbe3d6XT0AkhhJAyjKC+f1oRQgghRGjatGm4ffs25s+fX6epzmZmZnjz5g2cnJwwYcKEekxICCGENC40UkwIIYQQQgghpNGiopgQQgghhBBCSKNFRTEhhBBCCCGEkEaLimJCCCGEEEIIIY0WbbRFCCGEEEIIIaTRopFiQgghhBBCCCGNFhXFhBBCCCGEEEIaLSqKCSGEEEIIIYQ0WlQUE0IIIYQQQghptKgoJoQQQgghhBDSaFFRTAghhBBCCCGk0aKimBBCCCGEEEJIo0VFMSGEEEIIIYSQRuv/AWSw0vMFVALsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw seaborn stacked bar chart about number of succeeded tasks and number of failed tasks\n",
    "\n",
    "# save the plot\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "# sort by number of tasks\n",
    "task_df_agg = task_df_agg.sort_values(by='task_count', ascending=False)\n",
    "\n",
    "ax = task_df_agg[task_df_agg['app_name'] != 'total'].plot(kind='bar', x='app_name', y=['num_success', 'num_failed'], stacked=True, figsize=(10, 5), color=['#4daf4a', '#e41a1c'])\n",
    "\n",
    "# change legend names \n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, ['Success', 'Failed'], loc='upper right')\n",
    "\n",
    "\n",
    "plt.title('Number of planned tasks')\n",
    "plt.xlabel('App name')\n",
    "plt.ylabel('Number of tasks')\n",
    "plt.tight_layout()\n",
    "plt.savefig('/Users/greenmon/Dropbox/COINSE/#DroidAgent/DroidAgent_ICST_2024/figures/RQ2_num_tasks.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>success</th>\n",
       "      <th>num_actions</th>\n",
       "      <th>num_critiques</th>\n",
       "      <th>num_visited_pages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>APhotoManager</th>\n",
       "      <td>0.37</td>\n",
       "      <td>8.49</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ActivityDiary</th>\n",
       "      <td>0.66</td>\n",
       "      <td>8.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnkiDroid</th>\n",
       "      <td>0.59</td>\n",
       "      <td>9.43</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AntennaPod</th>\n",
       "      <td>0.64</td>\n",
       "      <td>7.36</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markor</th>\n",
       "      <td>0.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaterialFB</th>\n",
       "      <td>0.38</td>\n",
       "      <td>10.33</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MyExpenses</th>\n",
       "      <td>0.50</td>\n",
       "      <td>8.52</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omni-Notes</th>\n",
       "      <td>0.29</td>\n",
       "      <td>8.65</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenTracks</th>\n",
       "      <td>0.82</td>\n",
       "      <td>7.55</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phonograph</th>\n",
       "      <td>0.48</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarlet-Notes</th>\n",
       "      <td>0.35</td>\n",
       "      <td>10.80</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collect</th>\n",
       "      <td>0.51</td>\n",
       "      <td>7.77</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commons</th>\n",
       "      <td>0.44</td>\n",
       "      <td>8.50</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openlauncher</th>\n",
       "      <td>0.32</td>\n",
       "      <td>11.23</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osmeditor4android</th>\n",
       "      <td>0.57</td>\n",
       "      <td>8.27</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   success  num_actions  num_critiques  num_visited_pages\n",
       "app_name                                                                 \n",
       "APhotoManager         0.37         8.49           1.80               1.44\n",
       "ActivityDiary         0.66         8.34           1.66               1.79\n",
       "AnkiDroid             0.59         9.43           1.98               2.16\n",
       "AntennaPod            0.64         7.36           1.49               1.18\n",
       "Markor                0.43         9.11           1.86               1.29\n",
       "MaterialFB            0.38        10.33           2.21               1.25\n",
       "MyExpenses            0.50         8.52           1.76               2.43\n",
       "Omni-Notes            0.29         8.65           1.79               1.32\n",
       "OpenTracks            0.82         7.55           1.42               2.76\n",
       "Phonograph            0.48         8.67           1.88               1.88\n",
       "Scarlet-Notes         0.35        10.80           2.45               1.65\n",
       "collect               0.51         7.77           2.13               2.11\n",
       "commons               0.44         8.50           1.75               1.97\n",
       "openlauncher          0.32        11.23           2.58               1.26\n",
       "osmeditor4android     0.57         8.27           1.73               1.59"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_results = task_df.groupby('app_name')[['success', 'num_actions', 'num_critiques', 'num_visited_pages']].mean()\n",
    "\n",
    "# round to 2 decimal places in python pandas\n",
    "app_results.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>success</th>\n",
       "      <th>num_actions</th>\n",
       "      <th>num_critiques</th>\n",
       "      <th>num_visited_pages</th>\n",
       "      <th>task_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.466667</td>\n",
       "      <td>316.733333</td>\n",
       "      <td>68.066667</td>\n",
       "      <td>64.800000</td>\n",
       "      <td>36.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.166715</td>\n",
       "      <td>58.466678</td>\n",
       "      <td>16.816092</td>\n",
       "      <td>27.347499</td>\n",
       "      <td>8.790146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>82.500000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         success  num_actions  num_critiques  num_visited_pages  task_count\n",
       "count  15.000000    15.000000      15.000000          15.000000   15.000000\n",
       "mean   18.466667   316.733333      68.066667          64.800000   36.466667\n",
       "std     8.166715    58.466678      16.816092          27.347499    8.790146\n",
       "min     7.000000   216.000000      49.000000          30.000000   20.000000\n",
       "25%    11.000000   279.000000      55.000000          42.000000   31.500000\n",
       "50%    16.000000   317.000000      63.000000          62.000000   38.000000\n",
       "75%    25.500000   353.000000      75.000000          82.500000   43.000000\n",
       "max    31.000000   415.000000     113.000000         112.000000   53.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df_agg = task_df.groupby(['app_name']).sum()\n",
    "task_df_agg = task_df_agg.reset_index()\n",
    "\n",
    "# get summary statistics\n",
    "task_df_agg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>success</th>\n",
       "      <th>num_actions</th>\n",
       "      <th>num_critiques</th>\n",
       "      <th>num_visited_pages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.489675</td>\n",
       "      <td>8.868003</td>\n",
       "      <td>1.899351</td>\n",
       "      <td>1.738404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.145441</td>\n",
       "      <td>1.138554</td>\n",
       "      <td>0.324218</td>\n",
       "      <td>0.478024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>7.355556</td>\n",
       "      <td>1.421053</td>\n",
       "      <td>1.177778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.370427</td>\n",
       "      <td>8.307416</td>\n",
       "      <td>1.738636</td>\n",
       "      <td>1.304622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.484848</td>\n",
       "      <td>8.523810</td>\n",
       "      <td>1.804878</td>\n",
       "      <td>1.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.579545</td>\n",
       "      <td>9.269481</td>\n",
       "      <td>2.054674</td>\n",
       "      <td>2.040979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.815789</td>\n",
       "      <td>11.225806</td>\n",
       "      <td>2.580645</td>\n",
       "      <td>2.763158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         success  num_actions  num_critiques  num_visited_pages\n",
       "count  15.000000    15.000000      15.000000          15.000000\n",
       "mean    0.489675     8.868003       1.899351           1.738404\n",
       "std     0.145441     1.138554       0.324218           0.478024\n",
       "min     0.294118     7.355556       1.421053           1.177778\n",
       "25%     0.370427     8.307416       1.738636           1.304622\n",
       "50%     0.484848     8.523810       1.804878           1.650000\n",
       "75%     0.579545     9.269481       2.054674           2.040979\n",
       "max     0.815789    11.225806       2.580645           2.763158"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df.groupby(['app_name'])[['success', 'num_actions', 'num_critiques', 'num_visited_pages']].mean().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_11032/3159017908.py:27: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(ax=axes[0], x='app_name', y='task_count', data=task_df.groupby('app_name').sum().reset_index(), palette='Set2')\n",
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_11032/3159017908.py:37: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(ax=axes[1], x='App ID', y='num_actions', data=task_df, palette='Set2')\n",
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_11032/3159017908.py:41: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[1].set_xticklabels(axes[1].get_xticklabels(), fontsize=12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_name_map = {\n",
    "    \"ActivityDiary\": \"AD\",\n",
    "    \"AnkiDroid\": \"AK\",\n",
    "    \"AntennaPod\": \"AN\",\n",
    "    \"Markor\": \"MK\",\n",
    "    \"Omni-Notes\": \"ON\",\n",
    "    \"Phonograph\": \"PG\",\n",
    "    \"Scarlet-Notes\": \"SN\",\n",
    "    \"commons\": \"CM\",\n",
    "    \"openlauncher\": \"OP\",\n",
    "    \"osmeditor4android\": \"OM\",\n",
    "    \"MaterialFB\": \"MF\",\n",
    "    \"collect\": \"CL\",\n",
    "    \"APhotoManager\": \"AP\",\n",
    "    \"MyExpenses\": \"ME\",\n",
    "    \"OpenTracks\": \"OT\"\n",
    "}\n",
    "\n",
    "sns.set(font_scale=1.2)\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "# initialize figure \n",
    "fig, axes = plt.subplots(2, 1, figsize=(8, 3), sharex=False)\n",
    "\n",
    "task_df = task_df.sort_values(by='app_name')\n",
    "\n",
    "sns.barplot(ax=axes[0], x='app_name', y='task_count', data=task_df.groupby('app_name').sum().reset_index(), palette='Set2')\n",
    "axes[0].set_xlabel('')\n",
    "# hide xtickslabels\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_ylabel('# of tasks')\n",
    "axes[0].set_title('Number of Generated Tasks / Task Lengths')\n",
    "\n",
    "task_df['App ID'] = task_df['app_name'].apply(lambda x: app_name_map[x])\n",
    "task_df.sort_values(by='app_name', inplace=True)\n",
    "\n",
    "sns.boxplot(ax=axes[1], x='App ID', y='num_actions', data=task_df, palette='Set2')\n",
    "\n",
    "# change x-axis label for the second subplot\n",
    "axes[1].set_xlabel('')\n",
    "axes[1].set_xticklabels(axes[1].get_xticklabels(), fontsize=12)\n",
    "axes[1].set_ylabel('# of actions')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "plt.savefig('./figures/RQ2_task_quantitative.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn barplot by app_name for success ratio \n",
    "app_results.reset_index(inplace=True)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.title('Success ratio (by LLM reflection)')\n",
    "# axis rotation\n",
    "plt.xticks(rotation=90)\n",
    "sns.set_palette(\"Set2\")\n",
    "sns.barplot(x=app_results['app_name'], y=app_results['success'])\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('./figures/RQ2.1_success_ratio.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>success</th>\n",
       "      <th>num_actions</th>\n",
       "      <th>num_critiques</th>\n",
       "      <th>num_visited_pages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>app_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>APhotoManager</th>\n",
       "      <td>0.365854</td>\n",
       "      <td>8.487805</td>\n",
       "      <td>1.804878</td>\n",
       "      <td>1.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ActivityDiary</th>\n",
       "      <td>0.657895</td>\n",
       "      <td>8.342105</td>\n",
       "      <td>1.657895</td>\n",
       "      <td>1.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnkiDroid</th>\n",
       "      <td>0.590909</td>\n",
       "      <td>9.431818</td>\n",
       "      <td>1.977273</td>\n",
       "      <td>2.159091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AntennaPod</th>\n",
       "      <td>0.644444</td>\n",
       "      <td>7.355556</td>\n",
       "      <td>1.488889</td>\n",
       "      <td>1.177778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Markor</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>9.107143</td>\n",
       "      <td>1.857143</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaterialFB</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>2.208333</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MyExpenses</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.523810</td>\n",
       "      <td>1.761905</td>\n",
       "      <td>2.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omni-Notes</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>8.647059</td>\n",
       "      <td>1.794118</td>\n",
       "      <td>1.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenTracks</th>\n",
       "      <td>0.815789</td>\n",
       "      <td>7.552632</td>\n",
       "      <td>1.421053</td>\n",
       "      <td>2.763158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phonograph</th>\n",
       "      <td>0.484848</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>1.878788</td>\n",
       "      <td>1.878788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarlet-Notes</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>1.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collect</th>\n",
       "      <td>0.509434</td>\n",
       "      <td>7.773585</td>\n",
       "      <td>2.132075</td>\n",
       "      <td>2.113208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commons</th>\n",
       "      <td>0.437500</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>openlauncher</th>\n",
       "      <td>0.322581</td>\n",
       "      <td>11.225806</td>\n",
       "      <td>2.580645</td>\n",
       "      <td>1.258065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osmeditor4android</th>\n",
       "      <td>0.568182</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>1.590909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    success  num_actions  num_critiques  num_visited_pages\n",
       "app_name                                                                  \n",
       "APhotoManager      0.365854     8.487805       1.804878           1.439024\n",
       "ActivityDiary      0.657895     8.342105       1.657895           1.789474\n",
       "AnkiDroid          0.590909     9.431818       1.977273           2.159091\n",
       "AntennaPod         0.644444     7.355556       1.488889           1.177778\n",
       "Markor             0.428571     9.107143       1.857143           1.285714\n",
       "MaterialFB         0.375000    10.333333       2.208333           1.250000\n",
       "MyExpenses         0.500000     8.523810       1.761905           2.428571\n",
       "Omni-Notes         0.294118     8.647059       1.794118           1.323529\n",
       "OpenTracks         0.815789     7.552632       1.421053           2.763158\n",
       "Phonograph         0.484848     8.666667       1.878788           1.878788\n",
       "Scarlet-Notes      0.350000    10.800000       2.450000           1.650000\n",
       "collect            0.509434     7.773585       2.132075           2.113208\n",
       "commons            0.437500     8.500000       1.750000           1.968750\n",
       "openlauncher       0.322581    11.225806       2.580645           1.258065\n",
       "osmeditor4android  0.568182     8.272727       1.727273           1.590909"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_df.groupby('app_name')[['success', 'num_actions', 'num_critiques', 'num_visited_pages']].mean()"
   ]
  }
 ],
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